{"date_updated":"2024-08-12T08:26:35Z","corr_author":"1","citation":{"short":"J.A. Scott, H. Zakerinia, C. Lampert, in:, 12th International Conference on Learning Representations, OpenReview, 2024.","ista":"Scott JA, Zakerinia H, Lampert C. 2024. PEFLL: Personalized federated learning by learning to learn. 12th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","chicago":"Scott, Jonathan A, Hossein Zakerinia, and Christoph Lampert. “PEFLL: Personalized Federated Learning by Learning to Learn.” In <i>12th International Conference on Learning Representations</i>. OpenReview, 2024.","mla":"Scott, Jonathan A., et al. “PEFLL: Personalized Federated Learning by Learning to Learn.” <i>12th International Conference on Learning Representations</i>, OpenReview, 2024.","ieee":"J. A. Scott, H. Zakerinia, and C. Lampert, “PEFLL: Personalized federated learning by learning to learn,” in <i>12th International Conference on Learning Representations</i>, Vienna, Austria, 2024.","apa":"Scott, J. A., Zakerinia, H., &#38; Lampert, C. (2024). PEFLL: Personalized federated learning by learning to learn. In <i>12th International Conference on Learning Representations</i>. Vienna, Austria: OpenReview.","ama":"Scott JA, Zakerinia H, Lampert C. PEFLL: Personalized federated learning by learning to learn. In: <i>12th International Conference on Learning Representations</i>. OpenReview; 2024."},"title":"PEFLL: Personalized federated learning by learning to learn","type":"conference","acknowledgement":"This research was supported by the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp).\r\n","conference":{"start_date":"2024-03-07","name":"ICLR: International Conference on Learning Representations","location":"Vienna, Austria","end_date":"2024-03-07"},"department":[{"_id":"ChLa"}],"year":"2024","file_date_updated":"2024-08-12T07:38:06Z","quality_controlled":"1","abstract":[{"lang":"eng","text":"We present PeFLL, a new personalized federated learning algorithm that improves\r\nover the state-of-the-art in three aspects: 1) it produces more accurate models,\r\nespecially in the low-data regime, and not only for clients present during its\r\ntraining phase, but also for any that may emerge in the future; 2) it reduces the\r\namount of on-client computation and client-server communication by providing\r\nfuture clients with ready-to-use personalized models that require no additional\r\nfinetuning or optimization; 3) it comes with theoretical guarantees that establish\r\ngeneralization from the observed clients to future ones.\r\nAt the core of PeFLL lies a learning-to-learn approach that jointly trains an\r\nembedding network and a hypernetwork. The embedding network is used to\r\nrepresent clients in a latent descriptor space in a way that reflects their similarity\r\nto each other. The hypernetwork takes as input such descriptors and outputs the\r\nparameters of fully personalized client models. In combination, both networks\r\nconstitute a learning algorithm that achieves state-of-the-art performance in several\r\npersonalized federated learning benchmarks"}],"oa":1,"acknowledged_ssus":[{"_id":"ScienComp"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":"1","ddc":["000"],"date_created":"2024-08-11T22:01:12Z","status":"public","day":"07","publication":"12th International Conference on Learning Representations","_id":"17411","external_id":{"arxiv":["2306.05515"]},"language":[{"iso":"eng"}],"oa_version":"Published Version","author":[{"last_name":"Scott","first_name":"Jonathan A","full_name":"Scott, Jonathan A","id":"e499926b-f6e0-11ea-865d-9c63db0031e8"},{"last_name":"Zakerinia","id":"653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4","full_name":"Zakerinia, Hossein","first_name":"Hossein"},{"last_name":"Lampert","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph"}],"file":[{"creator":"dernst","checksum":"81b7ea2e667adaf9c7a7b6b376b1f251","access_level":"open_access","success":1,"content_type":"application/pdf","date_created":"2024-08-12T07:38:06Z","relation":"main_file","date_updated":"2024-08-12T07:38:06Z","file_name":"2024_ICLR_Scott.pdf","file_id":"17415","file_size":1029219}],"publisher":"OpenReview","publication_status":"published","date_published":"2024-03-07T00:00:00Z","has_accepted_license":"1","article_processing_charge":"No","month":"03"}