---
OA_place: publisher
OA_type: gold
_id: '22146'
abstract:
- lang: eng
  text: We study differentially private model training with stochastic gradient descent
    under learning rate scheduling and correlated noise. Although correlated noise,
    in particular via matrix factorizations, has been shown to improve accuracy, prior
    theoretical work focused primarily on the prefix-sum workload. That workload assumes
    a constant learning rate, whereas in practice learning rate schedules are widely
    used to accelerate training and improve convergence. We close this gap by deriving
    general upper and lower bounds for a broad class of learning rate schedules in
    both single- and multi-epoch settings. Building on these results, we propose a
    learning-rate-aware factorization that achieves improvements over prefix-sum factorizations
    under both MaxSE and MeanSE error metrics. Our theoretical analysis yields memory-efficient
    constructions suitable for practical deployment, and experiments on CIFAR-10 and
    IMDB datasets confirm that schedule-aware factorizations improve accuracy in private
    training.
acknowledgement: "We thank Rasmus Pagh, Christoph Lampert and Jalaj Upadhyay for valuable\r\ncomments
  on an early draft. We thank Ryan Mckenna for a fruitful discussion on the experiment\r\ndesign.
  We thank Antti Honkela for sharing insights on learning rate scheduling and DP.\r\nNikita
  P. Kalinin: Funded in part by the Austrian Science Fund (FWF) [10.55776/COE12].\r\nJoel
  Daniel Andersson: Funded by the European Union. Views and opinions expressed are
  however\r\nthose of the author(s) only and do not necessarily reflect those of the
  European Union or the European\r\nResearch Council Executive Agency. Neither the
  European Union nor the granting authority can be\r\nheld responsible for them. This
  project has received funding from the European Research Council\r\n(ERC) under the
  European Union’s Horizon 2020 research and innovation programme (MoDynStruct,\r\nNo.
  101019564). Additional funding by Providentia, a Data Science Distinguished Investigator
  grant\r\nfrom Novo Nordisk Fonden, with additional support from VILLUM Investigator
  grant 54451.\r\n"
alternative_title:
- LIPIcs
article_number: 2:1-2:21
article_processing_charge: No
arxiv: 1
author:
- first_name: Nikita
  full_name: Kalinin, Nikita
  id: 4b14526e-14d2-11ed-ba64-c14c9553d137
  last_name: Kalinin
- first_name: Joel D
  full_name: Andersson, Joel D
  id: 4a893819-d954-11f0-89b1-e360bad9ccc5
  last_name: Andersson
citation:
  ama: 'Kalinin N, Andersson JD. Learning rate scheduling with matrix factorization
    for private training. In: <i>7th Symposium on Foundations of Responsible Computing</i>.
    Vol 368. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2026. doi:<a href="https://doi.org/10.4230/LIPIcs.FORC.2026.2">10.4230/LIPIcs.FORC.2026.2</a>'
  apa: 'Kalinin, N., &#38; Andersson, J. D. (2026). Learning rate scheduling with
    matrix factorization for private training. In <i>7th Symposium on Foundations
    of Responsible Computing</i> (Vol. 368). Cambridge, MA; United States: Schloss
    Dagstuhl - Leibniz-Zentrum für Informatik. <a href="https://doi.org/10.4230/LIPIcs.FORC.2026.2">https://doi.org/10.4230/LIPIcs.FORC.2026.2</a>'
  chicago: Kalinin, Nikita, and Joel D Andersson. “Learning Rate Scheduling with Matrix
    Factorization for Private Training.” In <i>7th Symposium on Foundations of Responsible
    Computing</i>, Vol. 368. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2026.
    <a href="https://doi.org/10.4230/LIPIcs.FORC.2026.2">https://doi.org/10.4230/LIPIcs.FORC.2026.2</a>.
  ieee: N. Kalinin and J. D. Andersson, “Learning rate scheduling with matrix factorization
    for private training,” in <i>7th Symposium on Foundations of Responsible Computing</i>,
    Cambridge, MA; United States, 2026, vol. 368.
  ista: 'Kalinin N, Andersson JD. 2026. Learning rate scheduling with matrix factorization
    for private training. 7th Symposium on Foundations of Responsible Computing. FORC:
    Symposium on Foundations of Responsible Computing, LIPIcs, vol. 368, 2:1-2:21.'
  mla: Kalinin, Nikita, and Joel D. Andersson. “Learning Rate Scheduling with Matrix
    Factorization for Private Training.” <i>7th Symposium on Foundations of Responsible
    Computing</i>, vol. 368, 2:1-2:21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik,
    2026, doi:<a href="https://doi.org/10.4230/LIPIcs.FORC.2026.2">10.4230/LIPIcs.FORC.2026.2</a>.
  short: N. Kalinin, J.D. Andersson, in:, 7th Symposium on Foundations of Responsible
    Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2026.
conference:
  end_date: 2026-06-05
  location: Cambridge, MA; United States
  name: 'FORC: Symposium on Foundations of Responsible Computing'
  start_date: 2026-06-03
corr_author: '1'
das_tickbox: '0'
date_created: 2026-06-28T22:01:34Z
date_published: 2026-06-01T00:00:00Z
date_updated: 2026-09-16T07:37:21Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
- _id: GradSch
- _id: MoHe
doi: 10.4230/LIPIcs.FORC.2026.2
ec_funded: 1
external_id:
  arxiv:
  - '2511.17994'
file:
- access_level: open_access
  checksum: c661f016d3861a1c1b590b87a744d087
  content_type: application/pdf
  creator: dernst
  date_created: 2026-06-29T06:55:23Z
  date_updated: 2026-06-29T06:55:23Z
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file_date_updated: 2026-06-29T06:55:23Z
fulldoi: https://doi.org/10.4230/LIPIcs.FORC.2026.2
has_accepted_license: '1'
intvolume: '       368'
keyword:
- differential privacy
- machine learning
- matrix factorization
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62
  call_identifier: H2020
  grant_number: '101019564'
  name: The design and evaluation of modern fully dynamic data structures
- _id: d8f03aaa-b035-11f1-8588-d5147fa879e0
  grant_number: COE12
  name: Bilateral Artificial Intelligence (Lampert)
publication: 7th Symposium on Foundations of Responsible Computing
publication_identifier:
  eissn:
  - 1868-8969
  isbn:
  - '9783959774192'
publication_status: published
publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Learning rate scheduling with matrix factorization for private training
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 368
year: '2026'
...
---
OA_place: publisher
_id: '21198'
abstract:
- lang: eng
  text: "In recent years there has been a massive increase in the amount of data generated
    in a\r\ndecentralized manner. Ever more powerful edge devices, such as smartphones,
    have become\r\nubiquitous in most societies on earth. Through text typed, photos
    taken and apps used,\r\nthese devices, which we refer to as clients, generate
    enormous amounts of high quality and\r\ncomplex data. Moreover, the nature of
    these devices means the data they generate is often\r\nsensitive and privacy concerns
    prevent it being gathered and stored in a central location. This\r\npresents a
    challenge to the modern machine learning paradigm that requires central access\r\nto
    large amounts of data. Federated learning (FL) has emerged as one of the answers
    to\r\nthis problem. Rather than bringing the data to the model, FL sends the model
    to the data.\r\nModel training takes place on device, with periodically synchronized
    updates, allowing data to\r\nremain locally stored. While this approach offers
    significant privacy advantages it comes with\r\nits own set of unique challenges.
    These include: data heterogeneity, the notion that different\r\ndevices generate
    data in distinct ways which can negatively impact training dynamics; systems\r\nheterogeneity,
    meaning that different devices may have differing hardware specifications; high\r\ncommunication
    costs, which are induced by the repeated transferring of models over the\r\nnetwork
    and low device computational power, which limits the use of larger models on device.\r\nIn
    this thesis we present a range of methods for federated learning. We focus primarily
    on\r\nthe challenge of data heterogeneity, though the methods presented are designed
    to be well\r\nadapted to the other challenges of a federated setting, such as
    the constraints of limited\r\ncompute and communication overhead. We first present
    a method for explicitly modeling client\r\ndata heterogeneity. The approach formulates
    clients as samples from a certain probability\r\ndistribution and infers the parameters
    of this distribution from the available training clients.\r\nThis learned distribution
    then represents the heterogeneity present among the clients and can\r\nbe sampled
    from in order to create new simulated clients that are similar to the real clients
    we\r\nhave observed so far. Following this we present two methods for directly
    dealing with data\r\nheterogeneity through personalization. Highly heterogeneous
    client data distributions can mean\r\nthat learning a single global model becomes
    suboptimal, and some form of personalization of\r\nmodels to each individual client
    is required. Our approaches are based around hypernetworks,\r\nwhich we use to
    generate personalized model parameters without the need for additional\r\ntraining
    or finetuning. In the first approach we focus on generating full parameterizations
    of\r\nclient models using learned embeddings of client data and labels, with a
    hypernetwork located\r\non the central server. In the second approach we address
    the more challenging scenario where\r\nwe want to generate a personalized model
    for a client without any label information. The\r\nhypernetwork is trained to
    generate a low dimensional representation of a client’s personalized\r\nmodel
    parameters, allowing it to be transferred to and run on the client devices. In
    our final\r\npresented method, we change our focus and rather than aim to directly
    address the challenge\r\nof data heterogeneity, we instead ensure we are unaffected
    by it. This is done in the context\r\nof k-means clustering and we present a method
    for federated clustering with a focus on added\r\nprivacy guarantees."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "This research was funded in part by the Austrian Science Fund (FWF)\r\n[10.55776/COE12].
  Furthermore, the candidate acknowledges the support from the Scientific\r\nService
  Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp)."
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Jonathan A
  full_name: Scott, Jonathan A
  id: e499926b-f6e0-11ea-865d-9c63db0031e8
  last_name: Scott
citation:
  ama: Scott JA. Data heterogeneity and personalization in federated learning. 2026.
    doi:<a href="https://doi.org/10.15479/AT-ISTA-21198">10.15479/AT-ISTA-21198</a>
  apa: Scott, J. A. (2026). <i>Data heterogeneity and personalization in federated
    learning</i>. Institute of Science and Technology Austria. <a href="https://doi.org/10.15479/AT-ISTA-21198">https://doi.org/10.15479/AT-ISTA-21198</a>
  chicago: Scott, Jonathan A. “Data Heterogeneity and Personalization in Federated
    Learning.” Institute of Science and Technology Austria, 2026. <a href="https://doi.org/10.15479/AT-ISTA-21198">https://doi.org/10.15479/AT-ISTA-21198</a>.
  ieee: J. A. Scott, “Data heterogeneity and personalization in federated learning,”
    Institute of Science and Technology Austria, 2026.
  ista: Scott JA. 2026. Data heterogeneity and personalization in federated learning.
    Institute of Science and Technology Austria.
  mla: Scott, Jonathan A. <i>Data Heterogeneity and Personalization in Federated Learning</i>.
    Institute of Science and Technology Austria, 2026, doi:<a href="https://doi.org/10.15479/AT-ISTA-21198">10.15479/AT-ISTA-21198</a>.
  short: J.A. Scott, Data Heterogeneity and Personalization in Federated Learning,
    Institute of Science and Technology Austria, 2026.
corr_author: '1'
date_created: 2026-02-09T14:59:53Z
date_published: 2026-02-09T00:00:00Z
date_updated: 2026-10-01T09:29:03Z
day: '09'
ddc:
- '005'
degree_awarded: PhD
department:
- _id: GradSch
- _id: ChLa
doi: 10.15479/AT-ISTA-21198
doi_confirm: '1'
file:
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  date_created: 2026-02-17T11:46:22Z
  date_updated: 2026-02-17T11:46:22Z
  file_id: '21298'
  file_name: 2026_Scott_Jonathan_Thesis_Source.zip
  file_size: 272379252
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  date_created: 2026-02-27T10:25:41Z
  date_updated: 2026-02-27T10:25:41Z
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fulldoi: https://doi.org/10.15479/AT-ISTA-21198
has_accepted_license: '1'
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
page: '158'
project:
- _id: d8f03aaa-b035-11f1-8588-d5147fa879e0
  grant_number: COE12
  name: Bilateral Artificial Intelligence (Lampert)
publication_identifier:
  issn:
  - 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
  record:
  - id: '21207'
    relation: part_of_dissertation
    status: public
  - id: '20819'
    relation: part_of_dissertation
    status: public
  - id: '17411'
    relation: part_of_dissertation
    status: public
  - id: '18120'
    relation: part_of_dissertation
    status: public
status: public
supervisor:
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
title: Data heterogeneity and personalization in federated learning
type: dissertation
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2026'
...
---
OA_place: publisher
OA_type: diamond
_id: '20296'
abstract:
- lang: eng
  text: Learning-based systems are increasingly deployed across various domains, yet
    the complexity of traditional neural networks poses significant challenges for
    formal verification. Unlike conventional neural networks, learned Logic Gate Networks
    (LGNs) replace multiplications with Boolean logic gates, yielding a sparse, netlist-like
    architecture that is inherently more amenable to symbolic verification, while
    still delivering promising performance. In this paper, we introduce a SAT encoding
    for verifying global robustness and fairness in LGNs. We evaluate our method on
    five benchmark datasets, including a newly constructed 5-class variant, and find
    that LGNs are both verification-friendly and maintain strong predictive performance.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "This work is supported in part by the ERC grant under Grant No.
  ERC-2020-AdG 101020093 and\r\nthe Austrian Science Fund (FWF) [10.55776/COE12].
  This research was supported by the Scientific\r\nService Units (SSU) of ISTA through
  resources provided by Scientific Computing (SciComp)."
alternative_title:
- PMLR
article_number: '26'
article_processing_charge: No
arxiv: 1
author:
- first_name: Fabian
  full_name: Kresse, Fabian
  id: faff3c84-23f6-11ef-9085-e5187b51c604
  last_name: Kresse
- first_name: Zhengqi
  full_name: Yu, Zhengqi
  id: 20aa2ae8-f2f1-11ed-bbfa-8205053f1342
  last_name: Yu
  orcid: 0000-0002-4993-773X
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: Thomas A
  full_name: Henzinger, Thomas A
  id: 40876CD8-F248-11E8-B48F-1D18A9856A87
  last_name: Henzinger
  orcid: 0000-0002-2985-7724
citation:
  ama: 'Kresse F, Yu E, Lampert C, Henzinger TA. Logic gate neural networks are good
    for verification. In: <i>2nd International Conferenceon Neuro-Symbolic Systems</i>.
    Vol 288. ML Research Press; 2025.'
  apa: 'Kresse, F., Yu, E., Lampert, C., &#38; Henzinger, T. A. (2025). Logic gate
    neural networks are good for verification. In <i>2nd International Conferenceon
    Neuro-Symbolic Systems</i> (Vol. 288). Philadephia, PA, United States: ML Research
    Press.'
  chicago: Kresse, Fabian, Emily Yu, Christoph Lampert, and Thomas A Henzinger. “Logic
    Gate Neural Networks Are Good for Verification.” In <i>2nd International Conferenceon
    Neuro-Symbolic Systems</i>, Vol. 288. ML Research Press, 2025.
  ieee: F. Kresse, E. Yu, C. Lampert, and T. A. Henzinger, “Logic gate neural networks
    are good for verification,” in <i>2nd International Conferenceon Neuro-Symbolic
    Systems</i>, Philadephia, PA, United States, 2025, vol. 288.
  ista: 'Kresse F, Yu E, Lampert C, Henzinger TA. 2025. Logic gate neural networks
    are good for verification. 2nd International Conferenceon Neuro-Symbolic Systems.
    NeuS: International Conferenceon Neuro-Symbolic Systems, PMLR, vol. 288, 26.'
  mla: Kresse, Fabian, et al. “Logic Gate Neural Networks Are Good for Verification.”
    <i>2nd International Conferenceon Neuro-Symbolic Systems</i>, vol. 288, 26, ML
    Research Press, 2025.
  short: F. Kresse, E. Yu, C. Lampert, T.A. Henzinger, in:, 2nd International Conferenceon
    Neuro-Symbolic Systems, ML Research Press, 2025.
conference:
  end_date: 2025-05-30
  location: Philadephia, PA, United States
  name: 'NeuS: International Conferenceon Neuro-Symbolic Systems'
  start_date: 2025-05-28
corr_author: '1'
date_created: 2025-09-07T22:01:34Z
date_published: 2025-06-01T00:00:00Z
date_updated: 2026-09-16T07:02:18Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
- _id: ToHe
ec_funded: 1
external_id:
  arxiv:
  - '2505.19932'
file:
- access_level: open_access
  checksum: 90a32defed34787e771a5c1623b6b0d2
  content_type: application/pdf
  creator: dernst
  date_created: 2025-09-09T08:10:13Z
  date_updated: 2025-09-09T08:10:13Z
  file_id: '20314'
  file_name: 2025_NeuS_Kresse.pdf
  file_size: 295466
  relation: main_file
  success: 1
file_date_updated: 2025-09-09T08:10:13Z
has_accepted_license: '1'
intvolume: '       288'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
  call_identifier: H2020
  grant_number: '101020093'
  name: Vigilant Algorithmic Monitoring of Software
- _id: d8f03aaa-b035-11f1-8588-d5147fa879e0
  grant_number: COE12
  name: Bilateral Artificial Intelligence (Lampert)
publication: 2nd International Conferenceon Neuro-Symbolic Systems
publication_identifier:
  eissn:
  - 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Logic gate neural networks are good for verification
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 288
year: '2025'
...
---
OA_place: publisher
OA_type: gold
_id: '20819'
abstract:
- lang: eng
  text: "Clustering is a cornerstone of data analysis that is particularly suited
    to identifying coherent subgroups or substructures in unlabeled data, as are generated
    continuously in large amounts these days. However, in many cases traditional clustering
    methods are not applicable, because data are increasingly being produced and stored
    in a distributed way, e.g. on edge devices, and privacy concerns prevent it from
    being transferred to a central server. To address this challenge, we present FedDP-KMeans,
    a new algorithm for \r\n-means clustering that is fully-federated as well as differentially
    private. Our approach leverages (potentially small and out-of-distribution) server-side
    data to overcome the primary challenge of differentially private clustering methods:
    the need for a good initialization. Combining our initialization with a simple
    federated DP-Lloyds algorithm we obtain an algorithm that achieves excellent results
    on synthetic and real-world benchmark tasks. We also provide a theoretical analysis
    of our method that provides bounds on the convergence speed and cluster identification
    success."
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "This research was funded in part by the Austrian Science Fund (FWF)
  [10.55776/COE12] and supported by the Scientific Service Units (SSU) of ISTA through
  resources provided by Scientific Computing (SciComp).\r\n"
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Jonathan A
  full_name: Scott, Jonathan A
  id: e499926b-f6e0-11ea-865d-9c63db0031e8
  last_name: Scott
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: David
  full_name: Saulpic, David
  id: f8e48cf0-b0ff-11ed-b0e9-b4c35598f964
  last_name: Saulpic
citation:
  ama: 'Scott JA, Lampert C, Saulpic D. Differentially private federated k-means clustering
    with server-side data. In: <i>42nd International Conference on Machine Learning</i>.
    Vol 267. ML Research Press; 2025:53757-53790.'
  apa: 'Scott, J. A., Lampert, C., &#38; Saulpic, D. (2025). Differentially private
    federated k-means clustering with server-side data. In <i>42nd International Conference
    on Machine Learning</i> (Vol. 267, pp. 53757–53790). Vancouver, Canada: ML Research
    Press.'
  chicago: Scott, Jonathan A, Christoph Lampert, and David Saulpic. “Differentially
    Private Federated K-Means Clustering with Server-Side Data.” In <i>42nd International
    Conference on Machine Learning</i>, 267:53757–90. ML Research Press, 2025.
  ieee: J. A. Scott, C. Lampert, and D. Saulpic, “Differentially private federated
    k-means clustering with server-side data,” in <i>42nd International Conference
    on Machine Learning</i>, Vancouver, Canada, 2025, vol. 267, pp. 53757–53790.
  ista: 'Scott JA, Lampert C, Saulpic D. 2025. Differentially private federated k-means
    clustering with server-side data. 42nd International Conference on Machine Learning.
    ICML: International Conference on Machine Learning, PMLR, vol. 267, 53757–53790.'
  mla: Scott, Jonathan A., et al. “Differentially Private Federated K-Means Clustering
    with Server-Side Data.” <i>42nd International Conference on Machine Learning</i>,
    vol. 267, ML Research Press, 2025, pp. 53757–90.
  short: J.A. Scott, C. Lampert, D. Saulpic, in:, 42nd International Conference on
    Machine Learning, ML Research Press, 2025, pp. 53757–53790.
conference:
  end_date: 2025-07-19
  location: Vancouver, Canada
  name: 'ICML: International Conference on Machine Learning'
  start_date: 2025-07-13
corr_author: '1'
date_created: 2025-12-14T23:02:05Z
date_published: 2025-05-01T00:00:00Z
date_updated: 2026-09-16T07:26:12Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
- _id: MoHe
external_id:
  arxiv:
  - '2506.05408'
file:
- access_level: open_access
  checksum: 815b32b463023ca21e569c2158745c15
  content_type: application/pdf
  creator: dernst
  date_created: 2025-12-16T12:38:29Z
  date_updated: 2025-12-16T12:38:29Z
  file_id: '20829'
  file_name: 2025_ICML_Scott.pdf
  file_size: 746612
  relation: main_file
  success: 1
file_date_updated: 2025-12-16T12:38:29Z
has_accepted_license: '1'
intvolume: '       267'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 53757-53790
project:
- _id: d8f03aaa-b035-11f1-8588-d5147fa879e0
  grant_number: COE12
  name: Bilateral Artificial Intelligence (Lampert)
publication: 42nd International Conference on Machine Learning
publication_identifier:
  eissn:
  - 2640-3498
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
  record:
  - id: '21198'
    relation: dissertation_contains
    status: public
scopus_import: '1'
status: public
title: Differentially private federated k-means clustering with server-side data
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 267
year: '2025'
...
---
OA_place: publisher
OA_type: free access
_id: '22828'
abstract:
- lang: eng
  text: 'We propose Joint Moment Estimation (JME), a method for continually and privately
    estimating both the first and second moments of a data stream with reduced noise
    compared to naive approaches. JME supports the matrix mechanism and exploits a
    joint sensitivity analysis to identify a privacy regime in which the second-moment
    estimation incurs no additional privacy cost, thereby improving accuracy while
    maintaining privacy. We demonstrate JME’s effectiveness in two applications: estimating
    the running mean and covariance matrix for Gaussian density estimation and model
    training with DP-Adam.'
acknowledgement: "We thank Monika Henzinger for her valuable feedback and insightful
  discussions on earlier versions\r\nof this draft. We are also grateful to Mher Safaryan
  for his contributions to discussions on DP-Adam.\r\nAdditionally, we thank Ryan
  McKenna for suggesting Joint Clipping as a baseline.\r\nJalaj Upadhyay’s research
  was funded by the Rutgers Decanal Grant no. 302918, NSF CNS 2433628,\r\nGoogle Research
  Scholar Award, and Google Seed Fund Grant. A part of this work was done while\r\nvisiting
  the Institute of Science and Technology Austria (ISTA).\r\nNikita Kalinin’s research
  was funded in part by the Austrian Science Fund (FWF) [10.55776/COE12]"
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
author:
- first_name: Nikita
  full_name: Kalinin, Nikita
  id: 4b14526e-14d2-11ed-ba64-c14c9553d137
  last_name: Kalinin
- first_name: Jalaj
  full_name: Upadhyay, Jalaj
  last_name: Upadhyay
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Kalinin N, Upadhyay J, Lampert C. Continual release moment estimation with
    differential privacy. In: <i>Advances in Neural Information Processing Systems</i>.
    Vol 38. Neural Information Processing Systems Foundation; 2025:64002-64047. doi:<a
    href="https://doi.org/10.52202/085713-1924">10.52202/085713-1924</a>'
  apa: 'Kalinin, N., Upadhyay, J., &#38; Lampert, C. (2025). Continual release moment
    estimation with differential privacy. In <i>Advances in Neural Information Processing
    Systems</i> (Vol. 38, pp. 64002–64047). San Diego, CA, United States: Neural Information
    Processing Systems Foundation. <a href="https://doi.org/10.52202/085713-1924">https://doi.org/10.52202/085713-1924</a>'
  chicago: Kalinin, Nikita, Jalaj Upadhyay, and Christoph Lampert. “Continual Release
    Moment Estimation with Differential Privacy.” In <i>Advances in Neural Information
    Processing Systems</i>, 38:64002–47. Neural Information Processing Systems Foundation,
    2025. <a href="https://doi.org/10.52202/085713-1924">https://doi.org/10.52202/085713-1924</a>.
  ieee: N. Kalinin, J. Upadhyay, and C. Lampert, “Continual release moment estimation
    with differential privacy,” in <i>Advances in Neural Information Processing Systems</i>,
    San Diego, CA, United States, 2025, vol. 38, pp. 64002–64047.
  ista: 'Kalinin N, Upadhyay J, Lampert C. 2025. Continual release moment estimation
    with differential privacy. Advances in Neural Information Processing Systems.
    NeurIPS: Neural Information Processing Systems, Advances in Neural Information
    Processing Systems, vol. 38, 64002–64047.'
  mla: Kalinin, Nikita, et al. “Continual Release Moment Estimation with Differential
    Privacy.” <i>Advances in Neural Information Processing Systems</i>, vol. 38, Neural
    Information Processing Systems Foundation, 2025, pp. 64002–47, doi:<a href="https://doi.org/10.52202/085713-1924">10.52202/085713-1924</a>.
  short: N. Kalinin, J. Upadhyay, C. Lampert, in:, Advances in Neural Information
    Processing Systems, Neural Information Processing Systems Foundation, 2025, pp.
    64002–64047.
conference:
  location: San Diego, CA, United States
  name: 'NeurIPS: Neural Information Processing Systems'
corr_author: '1'
das_tickbox: '0'
date_created: 2026-09-06T22:02:00Z
date_published: 2025-12-02T00:00:00Z
date_updated: 2026-09-16T07:40:56Z
day: '02'
department:
- _id: GradSch
- _id: ChLa
doi: 10.52202/085713-1924
fulldoi: https://doi.org/10.52202/085713-1924
intvolume: '        38'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.52202/085713-1924
month: '12'
oa: 1
oa_version: Published Version
page: 64002-64047
project:
- _id: d8f03aaa-b035-11f1-8588-d5147fa879e0
  grant_number: COE12
  name: Bilateral Artificial Intelligence (Lampert)
publication: Advances in Neural Information Processing Systems
publication_identifier:
  isbn:
  - '9798331338275'
  issn:
  - 1049-5258
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Continual release moment estimation with differential privacy
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2025'
...
---
OA_place: publisher
OA_type: free access
_id: '22823'
abstract:
- lang: eng
  text: This position paper argues that setting the privacy budget in differential
    privacy should not be viewed as an important limitation of differential privacy
    compared to alternative methods for privacy-preserving machine learning. The so-called
    problem of interpreting the privacy budget is often presented as a major hindrance
    to the wider adoption of differential privacy in real-world deployments and is
    sometimes used to promote alternative mitigation techniques for data protection.
    We believe this misleads decision-makers into choosing unsafe methods. We argue
    that the difficulty in interpreting privacy budgets does not stem from the definition
    of differential privacy itself, but from the intrinsic difficulty of estimating
    privacy risks in context, a challenge that any rigorous method for privacy risk
    assessment face. Moreover, we claim that any sound method for estimating privacy
    risks should, given the current state of research, be expressible within the differential
    privacy framework or justify why it cannot.
acknowledgement: "This research was funded in whole by the Austrian Science Fund (FWF)
  10.55776/COE12. I thank\r\nAlberto Naibo for the discussion and his invitation to
  \"Logique, droit, IA : penser les algorithmes\"\r\ncolloquium. I thank Simone Bombari
  for the discussions. I thank EurIPS for the active poster session."
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
author:
- first_name: Edwige Audrey Lucienne
  full_name: Cyffers, Edwige Audrey Lucienne
  id: 20d4c299-977a-11ef-ae55-98b15ac64a57
  last_name: Cyffers
citation:
  ama: 'Cyffers EAL. Setting ε is not the issue in differential privacy. In: <i>39th
    Conference on Neural Information Processing Systems</i>. Vol 38. Neural Information
    Processing Systems Foundation; 2025:159016-159028. doi:<a href="https://doi.org/10.52202/085713-4798">10.52202/085713-4798</a>'
  apa: 'Cyffers, E. A. L. (2025). Setting ε is not the issue in differential privacy.
    In <i>39th Conference on Neural Information Processing Systems</i> (Vol. 38, pp.
    159016–159028). San Diego, CA, United States: Neural Information Processing Systems
    Foundation. <a href="https://doi.org/10.52202/085713-4798">https://doi.org/10.52202/085713-4798</a>'
  chicago: Cyffers, Edwige Audrey Lucienne. “Setting ε Is Not the Issue in Differential
    Privacy.” In <i>39th Conference on Neural Information Processing Systems</i>,
    38:159016–28. Neural Information Processing Systems Foundation, 2025. <a href="https://doi.org/10.52202/085713-4798">https://doi.org/10.52202/085713-4798</a>.
  ieee: E. A. L. Cyffers, “Setting ε is not the issue in differential privacy,” in
    <i>39th Conference on Neural Information Processing Systems</i>, San Diego, CA,
    United States, 2025, vol. 38, pp. 159016–159028.
  ista: 'Cyffers EAL. 2025. Setting ε is not the issue in differential privacy. 39th
    Conference on Neural Information Processing Systems. NeurIPS: Neural Information
    Processing Systems, Advances in Neural Information Processing Systems, vol. 38,
    159016–159028.'
  mla: Cyffers, Edwige Audrey Lucienne. “Setting ε Is Not the Issue in Differential
    Privacy.” <i>39th Conference on Neural Information Processing Systems</i>, vol.
    38, Neural Information Processing Systems Foundation, 2025, pp. 159016–28, doi:<a
    href="https://doi.org/10.52202/085713-4798">10.52202/085713-4798</a>.
  short: E.A.L. Cyffers, in:, 39th Conference on Neural Information Processing Systems,
    Neural Information Processing Systems Foundation, 2025, pp. 159016–159028.
conference:
  end_date: 2025-12-07
  location: San Diego, CA, United States
  name: 'NeurIPS: Neural Information Processing Systems'
  start_date: 2025-12-02
corr_author: '1'
das_tickbox: '0'
date_created: 2026-09-06T22:01:59Z
date_published: 2025-12-02T00:00:00Z
date_updated: 2026-10-15T09:34:50Z
day: '02'
department:
- _id: ChLa
doi: 10.52202/085713-4798
fulldoi: https://doi.org/10.52202/085713-4798
intvolume: '        38'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.52202/085713-4798
month: '12'
oa: 1
oa_version: Published Version
page: 159016-159028
project:
- _id: d8f03aaa-b035-11f1-8588-d5147fa879e0
  grant_number: COE12
  name: Bilateral Artificial Intelligence (Lampert)
publication: 39th Conference on Neural Information Processing Systems
publication_identifier:
  eissn:
  - 1049-5258
  isbn:
  - '9798331338275'
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Setting ε is not the issue in differential privacy
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2025'
...