{"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/53","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":53,"pages_in_order":68,"rows_per_page":100,"rows":[5201,5300],"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/52","next":"/task/federated-learning/papers/54","papers":[{"url":null,"slug":"decentralized-gossip-based-stochastic-bilevel","title":"Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks","date":"2022-06-22","arxiv_id":"2206.10870","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-latent-class-regression-for","title":"Federated Latent Class Regression for Hierarchical Data","date":"2022-06-22","arxiv_id":"2206.10783","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedoras-federated-architecture-search-under","title":"FedorAS: Federated Architecture Search under system heterogeneity","date":"2022-06-22","arxiv_id":"2206.11239","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantization-robust-federated-learning-for","title":"Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices","date":"2022-06-22","arxiv_id":"2206.10844","repositories_listed":0,"syntology":null},{"url":null,"slug":"texttt-fedbc-calibrating-global-and-local","title":"FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus","date":"2022-06-22","arxiv_id":"2206.10815","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-theory-for-federated-optimization","title":"A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates","date":"2022-06-21","arxiv_id":"2206.10189","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-industrial-federated-learning","title":"An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application","date":"2022-06-21","arxiv_id":"2206.13398","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-energy-and-carbon-footprint-analysis-of","title":"An Energy and Carbon Footprint Analysis of Distributed and Federated Learning","date":"2022-06-21","arxiv_id":"2206.10380","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedhisyn-a-hierarchical-synchronous-federated","title":"FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity","date":"2022-06-21","arxiv_id":"2206.10546","repositories_listed":0,"syntology":null},{"url":null,"slug":"sqsgd-locally-private-and-communication","title":"sqSGD: Locally Private and Communication Efficient Federated Learning","date":"2022-06-21","arxiv_id":"2206.10565","repositories_listed":0,"syntology":null},{"url":null,"slug":"wrapperfl-a-model-agnostic-plug-in-for","title":"WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning","date":"2022-06-21","arxiv_id":"2206.10407","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedsso-a-federated-server-side-second-order","title":"FedSSO: A Federated Server-Side Second-Order Optimization Algorithm","date":"2022-06-20","arxiv_id":"2206.09576","repositories_listed":0,"syntology":null},{"url":null,"slug":"quafl-federated-averaging-can-be-both","title":"Communication-Efficient Federated Learning With Data and Client Heterogeneity","date":"2022-06-20","arxiv_id":"2206.10032","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupled-federated-learning-for-asr-with-non","title":"Decoupled Federated Learning for ASR with Non-IID Data","date":"2022-06-18","arxiv_id":"2206.09102","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-privacy-preserving-federated","title":"Secure Embedding Aggregation for Federated Representation Learning","date":"2022-06-18","arxiv_id":"2206.09097","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-with-incremental","title":"Federated learning with incremental clustering for heterogeneous data","date":"2022-06-17","arxiv_id":"2206.08752","repositories_listed":0,"syntology":null},{"url":null,"slug":"blindfl-vertical-federated-machine-learning","title":"BlindFL: Vertical Federated Machine Learning without Peeking into Your Data","date":"2022-06-16","arxiv_id":"2206.07975","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-vfl-communication-efficient-1","title":"Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data","date":"2022-06-16","arxiv_id":"2206.08330","repositories_listed":0,"syntology":null},{"url":null,"slug":"sharper-convergence-guarantees-for","title":"Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning","date":"2022-06-16","arxiv_id":"2206.08307","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-adversarial-images-to-improve-outcomes","title":"Using adversarial images to improve outcomes of federated learning for non-IID data","date":"2022-06-16","arxiv_id":"2206.08124","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-federated-learning-via-predictive","title":"Federated Learning with Uncertainty via Distilled Predictive Distributions","date":"2022-06-15","arxiv_id":"2206.07562","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustered-scheduling-and-communication","title":"Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning","date":"2022-06-15","arxiv_id":"2206.07631","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-convergence-of-federated-learning-for","title":"Global Convergence of Federated Learning for Mixed Regression","date":"2022-06-15","arxiv_id":"2206.07279","repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-federated-learning-for-tackling","title":"Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring","date":"2022-06-14","arxiv_id":"2206.06818","repositories_listed":0,"syntology":null},{"url":null,"slug":"matching-pursuit-based-scheduling-for-over","title":"Matching Pursuit Based Scheduling for Over-the-Air Federated Learning","date":"2022-06-14","arxiv_id":"2206.06679","repositories_listed":0,"syntology":null},{"url":null,"slug":"computation-offloading-and-resource-1","title":"Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach","date":"2022-06-13","arxiv_id":"2206.05881","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-popularity-prediction-in-fog-rans-a","title":"Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach","date":"2022-06-13","arxiv_id":"2206.05894","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-bayesian-neural-regression-a","title":"Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process","date":"2022-06-13","arxiv_id":"2206.06357","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-on-riemannian-manifolds","title":"Federated Learning on Riemannian Manifolds","date":"2022-06-12","arxiv_id":"2206.05668","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-robust-federated-1","title":"Communication-Efficient Robust Federated Learning with Noisy Labels","date":"2022-06-11","arxiv_id":"2206.05558","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-with-gan-based-data-1","title":"Federated Learning with GAN-based Data Synthesis for Non-IID Clients","date":"2022-06-11","arxiv_id":"2206.05507","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-with-research-prototypes","title":"Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology","date":"2022-06-11","arxiv_id":"2206.05617","repositories_listed":0,"syntology":null},{"url":null,"slug":"mammodl-mammographic-breast-density","title":"MammoFL: Mammographic Breast Density Estimation using Federated Learning","date":"2022-06-11","arxiv_id":"2206.05575","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-leakage-from-model-in-federated-learning","title":"Deep Leakage from Model in Federated Learning","date":"2022-06-10","arxiv_id":"2206.04887","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-deep-autoencoder-for-federated-learning","title":"Fast Deep Autoencoder for Federated learning","date":"2022-06-10","arxiv_id":"2206.05136","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-federated-learning-with-privacy","title":"Hierarchical Federated Learning with Privacy","date":"2022-06-10","arxiv_id":"2206.05209","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-convergence-of-fedprox-local-dissimilarity","title":"On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond","date":"2022-06-10","arxiv_id":"2206.05187","repositories_listed":0,"syntology":null},{"url":null,"slug":"hidenseek-federated-lottery-ticket-via-server","title":"HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask","date":"2022-06-09","arxiv_id":"2206.04385","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-centric-data-federated-learning","title":"Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance","date":"2022-06-09","arxiv_id":"2206.04731","repositories_listed":0,"syntology":null},{"url":null,"slug":"mobility-improves-the-convergence-of","title":"Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying","date":"2022-06-09","arxiv_id":"2206.04742","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-unreasonable-effectiveness-of-2","title":"On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data","date":"2022-06-09","arxiv_id":"2206.04723","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-obfuscation-gives-a-false-sense-of","title":"Gradient Obfuscation Gives a False Sense of Security in Federated Learning","date":"2022-06-08","arxiv_id":"2206.04055","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-newton-type-methods-with","title":"Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation","date":"2022-06-07","arxiv_id":"2206.03588","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedpop-a-bayesian-approach-for-personalised","title":"FedPop: A Bayesian Approach for Personalised Federated Learning","date":"2022-06-07","arxiv_id":"2206.03611","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedrel-an-adaptive-federated-relevance","title":"An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning","date":"2022-06-07","arxiv_id":"2206.03420","repositories_listed":0,"syntology":null},{"url":null,"slug":"fel-high-capacity-learning-for-recommendation","title":"FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning","date":"2022-06-07","arxiv_id":"2206.03852","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-privacy-for-personalized-federated","title":"Group privacy for personalized federated learning","date":"2022-06-07","arxiv_id":"2206.03396","repositories_listed":0,"syntology":null},{"url":null,"slug":"subject-granular-differential-privacy-in","title":"Subject Granular Differential Privacy in Federated Learning","date":"2022-06-07","arxiv_id":"2206.03617","repositories_listed":0,"syntology":null},{"url":null,"slug":"subject-membership-inference-attacks-in","title":"Subject Membership Inference Attacks in Federated Learning","date":"2022-06-07","arxiv_id":"2206.03317","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-optimal-transport-approach-to-personalized","title":"An Optimal Transport Approach to Personalized Federated Learning","date":"2022-06-06","arxiv_id":"2206.02468","repositories_listed":0,"syntology":null},{"url":null,"slug":"fednst-federated-noisy-student-training-for","title":"FedNST: Federated Noisy Student Training for Automatic Speech Recognition","date":"2022-06-06","arxiv_id":"2206.02797","repositories_listed":0,"syntology":null},{"url":null,"slug":"interference-management-for-over-the-air","title":"Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks","date":"2022-06-06","arxiv_id":"2206.02398","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-adversarial-training-with","title":"Federated Adversarial Training with Transformers","date":"2022-06-05","arxiv_id":"2206.02131","repositories_listed":0,"syntology":null},{"url":null,"slug":"impossibility-of-collective-intelligence","title":"(Im)possibility of Collective Intelligence","date":"2022-06-05","arxiv_id":"2206.02786","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-architectures-for-distributed-machine","title":"Distributed Machine Learning in D2D-Enabled Heterogeneous Networks: Architectures, Performance, and Open Challenges","date":"2022-06-04","arxiv_id":"2206.01906","repositories_listed":0,"syntology":null},{"url":null,"slug":"scheduling-for-ground-assisted-federated","title":"Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations","date":"2022-06-04","arxiv_id":"2206.01952","repositories_listed":0,"syntology":null},{"url":null,"slug":"uav-aided-multi-community-federated-learning","title":"UAV-Aided Multi-Community Federated Learning","date":"2022-06-04","arxiv_id":"2206.02043","repositories_listed":0,"syntology":null},{"url":null,"slug":"completion-time-minimization-of-fog-ran","title":"Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission","date":"2022-06-03","arxiv_id":"2206.01373","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-generalization-of-wasserstein-robust-1","title":"On the Generalization of Wasserstein Robust Federated Learning","date":"2022-06-03","arxiv_id":"2206.01432","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-group-learning-distributed-weighting","title":"Towards Group Learning: Distributed Weighting of Experts","date":"2022-06-03","arxiv_id":"2206.02566","repositories_listed":0,"syntology":null},{"url":null,"slug":"applied-federated-learning-architectural","title":"Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings","date":"2022-06-02","arxiv_id":"2206.00807","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-with-a-sampling-algorithm","title":"Federated Learning with a Sampling Algorithm under Isoperimetry","date":"2022-06-02","arxiv_id":"2206.00920","repositories_listed":0,"syntology":null},{"url":null,"slug":"hex-human-in-the-loop-explainability-via-deep","title":"HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning","date":"2022-06-02","arxiv_id":"2206.01343","repositories_listed":0,"syntology":null},{"url":null,"slug":"resource-allocation-for-compression-aided","title":"Resource Allocation for Compression-aided Federated Learning with High Distortion Rate","date":"2022-06-02","arxiv_id":"2206.06976","repositories_listed":0,"syntology":null},{"url":null,"slug":"defense-against-gradient-leakage-attacks-via","title":"Defense Against Gradient Leakage Attacks via Learning to Obscure Data","date":"2022-06-01","arxiv_id":"2206.00769","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-in-satellite","title":"Federated Learning in Satellite Constellations","date":"2022-06-01","arxiv_id":"2206.00307","repositories_listed":0,"syntology":null},{"url":null,"slug":"walk-for-learning-a-random-walk-approach-for","title":"Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data","date":"2022-06-01","arxiv_id":"2206.00737","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-computation-and-communication-efficient","title":"A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting","date":"2022-05-31","arxiv_id":"2205.15580","repositories_listed":0,"syntology":null},{"url":null,"slug":"asynchronous-hierarchical-federated-learning","title":"Asynchronous Hierarchical Federated Learning","date":"2022-05-31","arxiv_id":"2206.00054","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-data-based-self-supervised-federated","title":"Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images","date":"2022-05-31","arxiv_id":"2205.15530","repositories_listed":0,"syntology":null},{"url":null,"slug":"secure-federated-clustering","title":"Secure Federated Clustering","date":"2022-05-31","arxiv_id":"2205.15564","repositories_listed":0,"syntology":null},{"url":"/paper/semi-supervised-cross-silo-advertising-with","slug":"semi-supervised-cross-silo-advertising-with","title":"VFed-SSD: Towards Practical Vertical Federated Advertising","date":"2022-05-31","arxiv_id":"2205.15987","repositories_listed":0,"syntology":null},{"url":null,"slug":"confederated-learning-federated-learning-with","title":"Confederated Learning: Federated Learning with Decentralized Edge Servers","date":"2022-05-30","arxiv_id":"2205.14905","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedauxfdp-differentially-private-one-shot","title":"FedAUXfdp: Differentially Private One-Shot Federated Distillation","date":"2022-05-30","arxiv_id":"2205.14960","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-federate-or-not-to-federate-incentivizing","title":"Maximizing Global Model Appeal in Federated Learning","date":"2022-05-30","arxiv_id":"2205.14840","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fair-federated-recommendation","title":"Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity","date":"2022-05-30","arxiv_id":"2206.02633","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-federated-learning-with-spike","title":"Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition","date":"2022-05-28","arxiv_id":"2205.14315","repositories_listed":0,"syntology":null},{"url":null,"slug":"fadman-federated-anomaly-detection-across","title":"FadMan: Federated Anomaly Detection across Multiple Attributed Networks","date":"2022-05-27","arxiv_id":"2205.14196","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedavg-with-fine-tuning-local-updates-lead-to","title":"FedAvg with Fine Tuning: Local Updates Lead to Representation Learning","date":"2022-05-27","arxiv_id":"2205.13692","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedcontrol-when-control-theory-meets","title":"FedControl: When Control Theory Meets Federated Learning","date":"2022-05-27","arxiv_id":"2205.14236","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-communication-learning-trade-off-for","title":"Towards Communication-Learning Trade-off for Federated Learning at the Network Edge","date":"2022-05-27","arxiv_id":"2205.14271","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fair-federated-learning-framework-with","title":"A Fair Federated Learning Framework With Reinforcement Learning","date":"2022-05-26","arxiv_id":"2205.13415","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-analysis-of-federated-learning-with","title":"A Unified Analysis of Federated Learning with Arbitrary Client Participation","date":"2022-05-26","arxiv_id":"2205.13648","repositories_listed":0,"syntology":null},{"url":null,"slug":"aggregating-gradients-in-encoded-domain-for","title":"Encoded Gradients Aggregation against Gradient Leakage in Federated Learning","date":"2022-05-26","arxiv_id":"2205.13216","repositories_listed":0,"syntology":null},{"url":null,"slug":"cali3f-calibrated-fast-fair-federated","title":"Cali3F: Calibrated Fast Fair Federated Recommendation System","date":"2022-05-26","arxiv_id":"2205.13121","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-non-negative-matrix-factorization","title":"Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information","date":"2022-05-26","arxiv_id":"2205.13300","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-split-bert-for-heterogeneous-text","title":"Federated Split BERT for Heterogeneous Text Classification","date":"2022-05-26","arxiv_id":"2205.13299","repositories_listed":0,"syntology":null},{"url":null,"slug":"friends-to-help-saving-federated-learning","title":"Combating Client Dropout in Federated Learning via Friend Model Substitution","date":"2022-05-26","arxiv_id":"2205.13222","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-federated-learning-joint-decentralized","title":"Mixed Federated Learning: Joint Decentralized and Centralized Learning","date":"2022-05-26","arxiv_id":"2205.13655","repositories_listed":0,"syntology":null},{"url":null,"slug":"quick-fl-quick-unbiased-compression-for","title":"QUIC-FL: Quick Unbiased Compression for Federated Learning","date":"2022-05-26","arxiv_id":"2205.13341","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-adaptation-of-reservoirs-via","title":"Federated Adaptation of Reservoirs via Intrinsic Plasticity","date":"2022-05-25","arxiv_id":"2206.11087","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-self-supervised-learning-for-1","title":"Federated Self-supervised Learning for Heterogeneous Clients","date":"2022-05-25","arxiv_id":"2205.12493","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-and-low-latency-federated-learning","title":"Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking","date":"2022-05-25","arxiv_id":"2205.13054","repositories_listed":0,"syntology":null},{"url":null,"slug":"verifi-towards-verifiable-federated","title":"VeriFi: Towards Verifiable Federated Unlearning","date":"2022-05-25","arxiv_id":"2205.12709","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-auc-computation-in","title":"Differentially Private AUC Computation in Vertical Federated Learning","date":"2022-05-24","arxiv_id":"2205.12412","repositories_listed":0,"syntology":null},{"url":null,"slug":"wireless-ad-hoc-federated-learning-a-fully","title":"Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning","date":"2022-05-24","arxiv_id":"2205.11779","repositories_listed":0,"syntology":null},{"url":null,"slug":"celest-federated-learning-for-globally","title":"CELEST: Federated Learning for Globally Coordinated Threat Detection","date":"2022-05-23","arxiv_id":"2205.11459","repositories_listed":0,"syntology":null},{"url":null,"slug":"fed-dart-and-fact-a-solution-for-federated","title":"Fed-DART and FACT: A solution for Federated Learning in a production environment","date":"2022-05-23","arxiv_id":"2205.11267","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-distillation-based-indoor","title":"Federated Distillation based Indoor Localization for IoT Networks","date":"2022-05-23","arxiv_id":"2205.11440","repositories_listed":0,"syntology":null},{"url":null,"slug":"fednorm-modality-based-normalization-in","title":"FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation","date":"2022-05-23","arxiv_id":"2205.11096","repositories_listed":0,"syntology":null}],"record_sha256":"2eb4a465c045b9bac5e184ae44f41f9c96c2e2cfdba69548a06967a27b11f8d6","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}