{"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/fedml-he-an-efficient-homomorphic-encryption","title":"FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System","arxiv_id":"2303.10837","date":"2023-03-20","proceeding":null,"authors":["Weizhao Jin","Yuhang Yao","Shanshan Han","Jiajun Gu","Carlee Joe-Wong","Srivatsan Ravi","Salman Avestimehr","Chaoyang He"],"abstract":"Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated local models on the server may reveal sensitive personal information by inversion attacks. Privacy-preserving methods, such as homomorphic encryption (HE), then become necessary for FL training. Despite HE's privacy advantages, its applications suffer from impractical overheads, especially for foundation models. In this paper, we present FedML-HE, the first practical federated learning system with efficient HE-based secure model aggregation. FedML-HE proposes to selectively encrypt sensitive parameters, significantly reducing both computation and communication overheads during training while providing customizable privacy preservation. Our optimized system demonstrates considerable overhead reduction, particularly for large foundation models (e.g., ~10x reduction for ResNet-50, and up to ~40x reduction for BERT), demonstrating the potential for scalable HE-based FL deployment.","url_abs":"https://arxiv.org/abs/2303.10837v3","url_pdf":"https://arxiv.org/pdf/2303.10837v3.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":"fedml-he-an-efficient-homomorphic-encryption","repo_url":"https://github.com/FedML-AI/FedML","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"fedml-he-an-efficient-homomorphic-encryption","repo_url":"https://github.com/rahn80643/federated-learning-pytorch-he-smap","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"federated-learning","task_name":"Federated Learning"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.10837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10837"}},"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/FedML-AI/FedML","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rahn80643/federated-learning-pytorch-he-smap","reach":null}],"summary":{"ran_draft_wrong":1,"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"repositories":1},"listed":{"samples":1,"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":1,"samples":[{"code_sha256_prefix":"c8cbfa0b0b61665b","entry":"separate_layers","repo":"rahn80643/federated-learning-pytorch-he-smap","repo_kind":"listed","path":"src/he_utils.py","file_url":"https://github.com/rahn80643/federated-learning-pytorch-he-smap/blob/HEAD/src/he_utils.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":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"c8cbfa0b0b61665b"}},{"code_sha256_prefix":"443938d9ecd165f7","entry":"get_weights","repo":"FedML-AI/FedML","repo_kind":"official","path":"python/fedml/utils/model_utils.py","file_url":"https://github.com/FedML-AI/FedML/blob/HEAD/python/fedml/utils/model_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"443938d9ecd165f7"}},{"code_sha256_prefix":"9347667d190263e8","entry":"load_arguments","repo":"FedML-AI/FedML","repo_kind":"official","path":"python/fedml/arguments.py","file_url":"https://github.com/FedML-AI/FedML/blob/HEAD/python/fedml/arguments.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"9347667d190263e8"}},{"code_sha256_prefix":"e80a141054e3bf40","entry":"move_to_cpu","repo":"FedML-AI/FedML","repo_kind":"official","path":"python/fedml/utils/model_utils.py","file_url":"https://github.com/FedML-AI/FedML/blob/HEAD/python/fedml/utils/model_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"e80a141054e3bf40"}},{"code_sha256_prefix":"a279848b3acf1a92","entry":"move_to_gpu","repo":"FedML-AI/FedML","repo_kind":"official","path":"python/fedml/utils/model_utils.py","file_url":"https://github.com/FedML-AI/FedML/blob/HEAD/python/fedml/utils/model_utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a279848b3acf1a92"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}