{"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/splitfed-when-federated-learning-meets-split","title":"SplitFed: When Federated Learning Meets Split Learning","arxiv_id":"2004.12088","date":"2020-04-25","proceeding":null,"authors":["Chandra Thapa","M. A. P. Chamikara","Seyit Camtepe","Lichao Sun"],"abstract":"Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data. SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server. Moreover, the split model makes SL a better option for resource-constrained environments. However, SL performs slower than FL due to the relay-based training across multiple clients. In this regard, this paper presents a novel approach, named splitfed learning (SFL), that amalgamates the two approaches eliminating their inherent drawbacks, along with a refined architectural configuration incorporating differential privacy and PixelDP to enhance data privacy and model robustness. Our analysis and empirical results demonstrate that (pure) SFL provides similar test accuracy and communication efficiency as SL while significantly decreasing its computation time per global epoch than in SL for multiple clients. Furthermore, as in SL, its communication efficiency over FL improves with the number of clients. Besides, the performance of SFL with privacy and robustness measures is further evaluated under extended experimental settings.","url_abs":"https://arxiv.org/abs/2004.12088v5","url_pdf":"https://arxiv.org/pdf/2004.12088v5.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":"splitfed-when-federated-learning-meets-split","repo_url":"https://github.com/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"splitfed-when-federated-learning-meets-split","repo_url":"https://github.com/vinuni-vishc/feddct","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"federated-learning","task_name":"Federated Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2004.12088","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.12088"}},"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/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vinuni-vishc/feddct","reach":null}],"summary":{"ran_draft_wrong":1,"ran_fixture":1,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"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":3,"samples":[{"code_sha256_prefix":"cddf6b6e878cc6ae","entry":"FedAvg","repo":"chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning","repo_kind":"official","path":"SFLV1_ResNet_HAM10000.py","file_url":"https://github.com/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning/blob/HEAD/SFLV1_ResNet_HAM10000.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cddf6b6e878cc6ae"}},{"code_sha256_prefix":"36a01988eb899505","entry":"calculate_accuracy","repo":"chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning","repo_kind":"official","path":"SFLV1_ResNet_HAM10000.py","file_url":"https://github.com/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning/blob/HEAD/SFLV1_ResNet_HAM10000.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"36a01988eb899505"}},{"code_sha256_prefix":"5749364c6b3444e0","entry":"train_server","repo":"chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning","repo_kind":"official","path":"SFLV1_ResNet_HAM10000.py","file_url":"https://github.com/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning/blob/HEAD/SFLV1_ResNet_HAM10000.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5749364c6b3444e0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}