{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/federated-learning/papers/68","list_of":"/task/federated-learning","task":"Federated Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":68,"pages_in_order":68,"rows_per_page":100,"rows":[6701,6771],"of":6771,"counts":{"archive_papers_tagged":6771,"with_a_code_link":1815,"where_syntology_ran_a_sample":457,"not_listed_spam_title":0,"listed":6771,"listed_where_code_ran":457,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":380,"every_run_a_failure_of_syntologys_instrument":77,"listed_with_a_run_with_no_instrument_failure":380,"listed_every_run_a_failure_of_syntologys_instrument":77,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/federated-learning","prev":"/task/federated-learning/papers/67","next":null,"papers":[{"url":null,"slug":"safa-a-semi-asynchronous-protocol-for-fast","title":"SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead","date":"2019-10-03","arxiv_id":"1910.01355","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-federated-brain-tumour","title":"Privacy-preserving Federated Brain Tumour Segmentation","date":"2019-10-02","arxiv_id":"1910.00962","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedpaq-a-communication-efficient-federated","title":"FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization","date":"2019-09-28","arxiv_id":"1909.13014","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-federated-learning","title":"Active Federated Learning","date":"2019-09-27","arxiv_id":"1909.12641","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-user-representation-learning","title":"Federated User Representation Learning","date":"2019-09-27","arxiv_id":"1909.12535","repositories_listed":0,"syntology":null},{"url":null,"slug":"190911875","title":"Federated Learning in Mobile Edge Networks: A Comprehensive Survey","date":"2019-09-26","arxiv_id":"1909.11875","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-rank-training-of-deep-neural-networks-for-1","title":"Low Rank Training of Deep Neural Networks for Emerging Memory Technology","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-federated-learning-of-deep-networks-from","title":"On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"190909836","title":"Optimal query complexity for private sequential learning against eavesdropping","date":"2019-09-21","arxiv_id":"1909.09836","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-federated-graph-learning-for","title":"Towards Federated Graph Learning for Collaborative Financial Crimes Detection","date":"2019-09-19","arxiv_id":"1909.12946","repositories_listed":0,"syntology":null},{"url":null,"slug":"detailed-comparison-of-communication","title":"Detailed comparison of communication efficiency of split learning and federated learning","date":"2019-09-18","arxiv_id":"1909.09145","repositories_listed":0,"syntology":null},{"url":null,"slug":"measure-contribution-of-participants-in","title":"Measure Contribution of Participants in Federated Learning","date":"2019-09-17","arxiv_id":"1909.08525","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-meta-learning","title":"Differentially Private Meta-Learning","date":"2019-09-12","arxiv_id":"1909.05830","repositories_listed":0,"syntology":null},{"url":null,"slug":"byzantine-robust-federated-machine-learning","title":"Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging","date":"2019-09-11","arxiv_id":"1909.05125","repositories_listed":0,"syntology":null},{"url":null,"slug":"first-analysis-of-local-gd-on-heterogeneous","title":"First Analysis of Local GD on Heterogeneous Data","date":"2019-09-10","arxiv_id":"1909.04715","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-descent-with-compressed-iterates","title":"Gradient Descent with Compressed Iterates","date":"2019-09-10","arxiv_id":"1909.04716","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-federated-learning-across","title":"Hierarchical Federated Learning Across Heterogeneous Cellular Networks","date":"2019-09-05","arxiv_id":"1909.02362","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-demand-prediction-with-federated","title":"Energy Demand Prediction with Federated Learning for Electric Vehicle Networks","date":"2019-09-03","arxiv_id":"1909.00907","repositories_listed":0,"syntology":null},{"url":null,"slug":"rewarding-high-quality-data-via-influence","title":"Rewarding High-Quality Data via Influence Functions","date":"2019-08-30","arxiv_id":"1908.11598","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-end-to-end-encrypted-neural-network-for","title":"An End-to-End Encrypted Neural Network for Gradient Updates Transmission in Federated Learning","date":"2019-08-22","arxiv_id":"1908.08340","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-effective-device-aware-federated","title":"Towards Effective Device-Aware Federated Learning","date":"2019-08-20","arxiv_id":"1908.07420","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-federated-learning-approach-for-mobile","title":"A Federated Learning Approach for Mobile Packet Classification","date":"2019-07-30","arxiv_id":"1907.13113","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-wireless","title":"Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges","date":"2019-07-30","arxiv_id":"1908.06847","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-over-wireless-fading","title":"Federated Learning over Wireless Fading Channels","date":"2019-07-23","arxiv_id":"1907.09769","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedhealth-a-federated-transfer-learning","title":"FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare","date":"2019-07-22","arxiv_id":"1907.09173","repositories_listed":0,"syntology":null},{"url":null,"slug":"wireless-federated-distillation-for","title":"Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data","date":"2019-07-05","arxiv_id":"1907.02745","repositories_listed":0,"syntology":null},{"url":null,"slug":"mobile-edge-computing-blockchain-and","title":"Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices","date":"2019-06-26","arxiv_id":"1906.10893","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-solution-on-distributed-edge","title":"Active Learning Solution on Distributed Edge Computing","date":"2019-06-25","arxiv_id":"1906.10718","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-qoe-modeling-using","title":"Privacy Preserving QoE Modeling using Collaborative Learning","date":"2019-06-21","arxiv_id":"1906.09248","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-and-differentially-private","title":"Scalable and Differentially Private Distributed Aggregation in the Shuffled Model","date":"2019-06-19","arxiv_id":"1906.08320","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-federated-learning-in-a-heterogeneous","title":"Robust Federated Learning in a Heterogeneous Environment","date":"2019-06-16","arxiv_id":"1906.06629","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-federated-multi-task-learning","title":"Variational Federated Multi-Task Learning","date":"2019-06-14","arxiv_id":"1906.06268","repositories_listed":0,"syntology":null},{"url":null,"slug":"secure-federated-matrix-factorization","title":"Secure Federated Matrix Factorization","date":"2019-06-12","arxiv_id":"1906.05108","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-emoji-prediction-in-a","title":"Federated Learning for Emoji Prediction in a Mobile Keyboard","date":"2019-06-11","arxiv_id":"1906.04329","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-ai-lets-a-team-imagine-together","title":"Federated AI lets a team imagine together: Federated Learning of GANs","date":"2019-06-09","arxiv_id":"1906.03595","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-training-with-heterogeneous-data","title":"Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms","date":"2019-06-04","arxiv_id":"1906.01736","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600887","title":"Secure Distributed On-Device Learning Networks With Byzantine Adversaries","date":"2019-06-03","arxiv_id":"1906.00887","repositories_listed":0,"syntology":null},{"url":null,"slug":"190511796","title":"Self-supervised audio representation learning for mobile devices","date":"2019-05-24","arxiv_id":"1905.11796","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralized-bayesian-learning-over-graphs","title":"Decentralized Bayesian Learning over Graphs","date":"2019-05-24","arxiv_id":"1905.10466","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-fl-cooperative-learning-mechanism","title":"Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data","date":"2019-05-17","arxiv_id":"1905.07210","repositories_listed":0,"syntology":null},{"url":null,"slug":"braintorrent-a-peer-to-peer-environment-for","title":"BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning","date":"2019-05-16","arxiv_id":"1905.06731","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-resource-allocation-in-federated","title":"Fair Resource Allocation in Federated Learning","date":"2019-05-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incentive-design-for-efficient-federated","title":"Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach","date":"2019-05-16","arxiv_id":"1905.07479","repositories_listed":0,"syntology":null},{"url":null,"slug":"leaf-a-benchmark-for-federated-settings-1","title":"LEAF: A Benchmark for Federated Settings","date":"2019-05-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-federated-training-via-collaborative","title":"Robust Federated Training via Collaborative Machine Teaching using Trusted Instances","date":"2019-05-08","arxiv_id":"1905.02941","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-federated-neural-matching","title":"Probabilistic Federated Neural Matching","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-cyclic-stochastic-gradient-descent","title":"Semi-Cyclic Stochastic Gradient Descent","date":"2019-04-23","arxiv_id":"1904.10120","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-of-out-of-vocabulary-words","title":"Federated Learning Of Out-Of-Vocabulary Words","date":"2019-03-26","arxiv_id":"1903.10635","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-federated-deep","title":"Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation","date":"2019-03-18","arxiv_id":"1903.07424","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-shot-federated-learning","title":"One-Shot Federated Learning","date":"2019-02-28","arxiv_id":"1902.11175","repositories_listed":0,"syntology":null},{"url":null,"slug":"peer-to-peer-federated-learning-on-graphs","title":"Peer-to-peer Federated Learning on Graphs","date":"2019-01-31","arxiv_id":"1901.11173","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-via-over-the-air","title":"Federated Learning via Over-the-Air Computation","date":"2018-12-31","arxiv_id":"1812.11750","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-objective-evolutionary-federated","title":"Multi-objective Evolutionary Federated Learning","date":"2018-12-18","arxiv_id":"1812.07478","repositories_listed":0,"syntology":null},{"url":null,"slug":"no-peek-a-survey-of-private-distributed-deep","title":"No Peek: A Survey of private distributed deep learning","date":"2018-12-08","arxiv_id":"1812.03288","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-data-generative-models","title":"Differentially Private Data Generative Models","date":"2018-12-06","arxiv_id":"1812.02274","repositories_listed":0,"syntology":null},{"url":null,"slug":"protection-against-reconstruction-and-its","title":"Protection Against Reconstruction and Its Applications in Private Federated Learning","date":"2018-12-03","arxiv_id":"1812.00984","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-on-device-machine","title":"Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data","date":"2018-11-28","arxiv_id":"1811.11479","repositories_listed":0,"syntology":null},{"url":null,"slug":"fadlfederated-autonomous-deep-learning-for","title":"FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record","date":"2018-11-28","arxiv_id":"1811.11400","repositories_listed":0,"syntology":null},{"url":null,"slug":"partitioned-variational-inference-a-unified","title":"Partitioned Variational Inference: A unified framework encompassing federated and continual learning","date":"2018-11-27","arxiv_id":"1811.11206","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-mobile-computing-for-the-visually","title":"A Survey of Mobile Computing for the Visually Impaired","date":"2018-11-25","arxiv_id":"1811.10120","repositories_listed":0,"syntology":null},{"url":null,"slug":"west-word-encoded-sequence-transducers","title":"WEST: Word Encoded Sequence Transducers","date":"2018-11-20","arxiv_id":"1811.08417","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-in-distributed-medical","title":"Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data","date":"2018-10-19","arxiv_id":"1810.08553","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-institutional-deep-learning-modeling","title":"Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation","date":"2018-10-10","arxiv_id":"1810.04304","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-edge-ai-intelligentizing-mobile-edge","title":"In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning","date":"2018-09-19","arxiv_id":"1809.07857","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-and-controlling-user","title":"Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning","date":"2018-05-15","arxiv_id":"1805.05838","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-ultra-reliable-low","title":"Federated Learning for Ultra-Reliable Low-Latency V2V Communications","date":"2018-05-11","arxiv_id":"1805.09253","repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-resolution-and-federated-learning-get","title":"Entity Resolution and Federated Learning get a Federated Resolution","date":"2018-03-11","arxiv_id":"1803.04035","repositories_listed":0,"syntology":null},{"url":null,"slug":"dont-encrypt-the-data-just-approximate-the","title":"Don't encrypt the data; just approximate the model \\ Towards Secure Transaction and Fair Pricing of Training Data","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"private-federated-learning-on-vertically","title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","date":"2017-11-29","arxiv_id":"1711.10677","repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-secure-aggregation-for-federated","title":"Practical Secure Aggregation for Federated Learning on User-Held Data","date":"2016-11-14","arxiv_id":"1611.04482","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-strategies-for-improving","title":"Federated Learning: Strategies for Improving Communication Efficiency","date":"2016-10-18","arxiv_id":"1610.05492","repositories_listed":0,"syntology":null}],"record_sha256":"e22c635d37dd76f85b2e9084d1ff5d603a1537cf04d83ed7db91a631f4d3b1d2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}