{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adaptive-federated-optimization","title":"Adaptive Federated Optimization","arxiv_id":"2003.00295","date":"2020-02-29","proceeding":"ICLR 2021 1","authors":["Sashank Reddi","Zachary Charles","Manzil Zaheer","Zachary Garrett","Keith Rush","Jakub Konečný","Sanjiv Kumar","H. Brendan McMahan"],"abstract":"Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Standard federated optimization methods such as Federated Averaging (FedAvg) are often difficult to tune and exhibit unfavorable convergence behavior. In non-federated settings, adaptive optimization methods have had notable success in combating such issues. In this work, we propose federated versions of adaptive optimizers, including Adagrad, Adam, and Yogi, and analyze their convergence in the presence of heterogeneous data for general non-convex settings. Our results highlight the interplay between client heterogeneity and communication efficiency. We also perform extensive experiments on these methods and show that the use of adaptive optimizers can significantly improve the performance of federated learning.","url_abs":"https://arxiv.org/abs/2003.00295v5","url_pdf":"https://arxiv.org/pdf/2003.00295v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adaptive-federated-optimization","repo_url":"https://github.com/google-research/federated/tree/master/optimization","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"adaptive-federated-optimization","repo_url":"https://github.com/KarhouTam/FL-bench","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"adaptive-federated-optimization","repo_url":"https://github.com/adap/flower","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"adaptive-federated-optimization","repo_url":"https://github.com/alshedivat/fedpa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"adaptive-federated-optimization","repo_url":"https://github.com/intel/openfl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"adaptive-federated-optimization","repo_url":"https://github.com/securefederatedai/openfl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"adaptive-federated-optimization","repo_url":"https://github.com/vaseline555/Federated-Learning-in-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"adaptive-federated-optimization","repo_url":"https://github.com/wenh06/fl-sim","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"federated-learning","task_name":"Federated Learning"}],"methods":[{"method_slug":"adam","method_name":"Adam"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.00295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2003.00295"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/alshedivat/fedpa","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/wenh06/fl-sim","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/adap/flower","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/securefederatedai/openfl","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/KarhouTam/FL-bench","reach":{"status":"ok","spdx":"GPL-3.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/intel/openfl","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/google-research/federated/tree/master/optimization","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vaseline555/Federated-Learning-in-PyTorch","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"listed":{"samples":2,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"11666a4ae1749613","entry":"stratified_split","repo":"vaseline555/Federated-Learning-in-PyTorch","repo_kind":"listed","path":"src/utils.py","file_url":"https://github.com/vaseline555/Federated-Learning-in-PyTorch/blob/HEAD/src/utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"11666a4ae1749613"}},{"code_sha256_prefix":"26d709a4649d9aea","entry":"check_args","repo":"vaseline555/Federated-Learning-in-PyTorch","repo_kind":"listed","path":"src/utils.py","file_url":"https://github.com/vaseline555/Federated-Learning-in-PyTorch/blob/HEAD/src/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"26d709a4649d9aea"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}