{"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/federated-learning-with-matched-averaging-1","title":"Federated Learning with Matched Averaging","arxiv_id":"2002.06440","date":"2020-02-15","proceeding":"ICLR 2020 1","authors":["Hongyi Wang","Mikhail Yurochkin","Yuekai Sun","Dimitris Papailiopoulos","Yasaman Khazaeni"],"abstract":"Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need to store the data in the cloud. We propose Federated matched averaging (FedMA) algorithm designed for federated learning of modern neural network architectures e.g. convolutional neural networks (CNNs) and LSTMs. FedMA constructs the shared global model in a layer-wise manner by matching and averaging hidden elements (i.e. channels for convolution layers; hidden states for LSTM; neurons for fully connected layers) with similar feature extraction signatures. Our experiments indicate that FedMA not only outperforms popular state-of-the-art federated learning algorithms on deep CNN and LSTM architectures trained on real world datasets, but also reduces the overall communication burden.","url_abs":"https://arxiv.org/abs/2002.06440v1","url_pdf":"https://arxiv.org/pdf/2002.06440v1.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":"federated-learning-with-matched-averaging-1","repo_url":"https://github.com/IBM/FedMA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"federated-learning","task_name":"Federated Learning"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2002.06440","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.06440"}},"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/IBM/FedMA","reach":null}],"summary":{"ran_draft_wrong":2,"ran":2,"unverified":5},"by_repo_kind":{"official":{"samples":9,"ran":4,"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":"7a8a094a8b866225","entry":"collect_weights","repo":"IBM/FedMA","repo_kind":"official","path":"language_modeling/language_main.py","file_url":"https://github.com/IBM/FedMA/blob/HEAD/language_modeling/language_main.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7a8a094a8b866225"}},{"code_sha256_prefix":"b7f7a1b9782566a2","entry":"add_fit_args","repo":"IBM/FedMA","repo_kind":"official","path":"language_modeling/language_main.py","file_url":"https://github.com/IBM/FedMA/blob/HEAD/language_modeling/language_main.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b7f7a1b9782566a2"}},{"code_sha256_prefix":"9c648207429148d6","entry":"process_softmax_bias","repo":"IBM/FedMA","repo_kind":"official","path":"matching/pfnm.py","file_url":"https://github.com/IBM/FedMA/blob/HEAD/matching/pfnm.py","link_basis":"first_harvest_node","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":"9c648207429148d6"}},{"code_sha256_prefix":"cd6992373bd9ebe9","entry":"row_param_cost_simplified","repo":"IBM/FedMA","repo_kind":"official","path":"matching/pfnm.py","file_url":"https://github.com/IBM/FedMA/blob/HEAD/matching/pfnm.py","link_basis":"first_harvest_node","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":"cd6992373bd9ebe9"}},{"code_sha256_prefix":"0deab229a7b7de8e","entry":"compute_cost","repo":"IBM/FedMA","repo_kind":"official","path":"matching/pfnm.py","file_url":"https://github.com/IBM/FedMA/blob/HEAD/matching/pfnm.py","link_basis":"first_harvest_node","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":"0deab229a7b7de8e"}},{"code_sha256_prefix":"a4f89ab2ff25bf83","entry":"evaluate","repo":"IBM/FedMA","repo_kind":"official","path":"language_modeling/language_main.py","file_url":"https://github.com/IBM/FedMA/blob/HEAD/language_modeling/language_main.py","link_basis":"first_harvest_node","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":"a4f89ab2ff25bf83"}},{"code_sha256_prefix":"b31318bddc5412d3","entry":"fedma_whole","repo":"IBM/FedMA","repo_kind":"official","path":"matching/pfnm.py","file_url":"https://github.com/IBM/FedMA/blob/HEAD/matching/pfnm.py","link_basis":"first_harvest_node","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":"b31318bddc5412d3"}},{"code_sha256_prefix":"c355964843bedb50","entry":"match_layer","repo":"IBM/FedMA","repo_kind":"official","path":"matching/pfnm.py","file_url":"https://github.com/IBM/FedMA/blob/HEAD/matching/pfnm.py","link_basis":"first_harvest_node","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":"c355964843bedb50"}},{"code_sha256_prefix":"8a3d83e95a786484","entry":"matching_upd_j","repo":"IBM/FedMA","repo_kind":"official","path":"matching/pfnm.py","file_url":"https://github.com/IBM/FedMA/blob/HEAD/matching/pfnm.py","link_basis":"first_harvest_node","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":"8a3d83e95a786484"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}