{"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/48","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":48,"pages_in_order":68,"rows_per_page":100,"rows":[4701,4800],"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/47","next":"/task/federated-learning/papers/49","papers":[{"url":null,"slug":"vertical-federated-knowledge-transfer-via","title":"Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks","date":"2023-02-11","arxiv_id":"2302.05675","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-linear-speedup-in-non-iid-federated","title":"Achieving Linear Speedup in Non-IID Federated Bilevel Learning","date":"2023-02-10","arxiv_id":"2302.05412","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-against-agnostic-inference-attack-in","title":"Privacy Against Agnostic Inference Attacks in Vertical Federated Learning","date":"2023-02-10","arxiv_id":"2302.05545","repositories_listed":0,"syntology":null},{"url":null,"slug":"delay-sensitive-hierarchical-federated","title":"Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updates","date":"2023-02-09","arxiv_id":"2302.04851","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-field-terahertz-communications-model","title":"Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation","date":"2023-02-09","arxiv_id":"2302.04802","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fairer-and-more-efficient-federated","title":"Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models","date":"2023-02-09","arxiv_id":"2302.04464","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploratory-analysis-of-federated-learning","title":"Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III","date":"2023-02-08","arxiv_id":"2302.04208","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-model-consistency-of","title":"Improving the Model Consistency of Decentralized Federated Learning","date":"2023-02-08","arxiv_id":"2302.04083","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-with-regularized-client","title":"Federated Learning with Regularized Client Participation","date":"2023-02-07","arxiv_id":"2302.03662","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-variational-inference-methods-for","title":"Federated Variational Inference Methods for Structured Latent Variable Models","date":"2023-02-07","arxiv_id":"2302.03314","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-parameterization-of-deep-learning","title":"Adaptive Parameterization of Deep Learning Models for Federated Learning","date":"2023-02-06","arxiv_id":"2302.02949","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-fusion-rule-for-personalized-federated","title":"Cross-Fusion Rule for Personalized Federated Learning","date":"2023-02-06","arxiv_id":"2302.02531","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-convergence-of-federated-averaging","title":"On the Convergence of Federated Averaging with Cyclic Client Participation","date":"2023-02-06","arxiv_id":"2302.03109","repositories_listed":0,"syntology":null},{"url":null,"slug":"topology-aware-federated-learning-in-edge","title":"Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey","date":"2023-02-06","arxiv_id":"2302.02573","repositories_listed":0,"syntology":null},{"url":null,"slug":"z-signfedavg-a-unified-stochastic-sign-based","title":"$z$-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning","date":"2023-02-06","arxiv_id":"2302.02589","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-privacy-preserving-collaborative","title":"Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction","date":"2023-02-05","arxiv_id":"2303.04744","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-layer-federated-learning-optimization","title":"Digital Over-the-Air Federated Learning in Multi-Antenna Systems","date":"2023-02-04","arxiv_id":"2302.14648","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedspectral-spectral-clustering-using","title":"FedSpectral+: Spectral Clustering using Federated Learning","date":"2023-02-04","arxiv_id":"2302.02137","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-based-federated-learning-for-label","title":"GAN-based Vertical Federated Learning for Label Protection in Binary Classification","date":"2023-02-04","arxiv_id":"2302.02245","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-federated-knowledge-graph","title":"Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning","date":"2023-02-04","arxiv_id":"2302.02069","repositories_listed":0,"syntology":null},{"url":null,"slug":"use-of-federated-learning-and-blockchain","title":"Use of Federated Learning and Blockchain towards Securing Financial Services","date":"2023-02-04","arxiv_id":"2303.12944","repositories_listed":0,"syntology":null},{"url":null,"slug":"convergence-analysis-of-split-learning-on-non","title":"Convergence Analysis of Sequential Split Learning on Heterogeneous Data","date":"2023-02-03","arxiv_id":"2302.01633","repositories_listed":0,"syntology":null},{"url":null,"slug":"gtv-generating-tabular-data-via-vertical","title":"GTV: Generating Tabular Data via Vertical Federated Learning","date":"2023-02-03","arxiv_id":"2302.01706","repositories_listed":0,"syntology":null},{"url":null,"slug":"vertical-federated-learning-taxonomies","title":"Vertical Federated Learning: Taxonomies, Threats, and Prospects","date":"2023-02-03","arxiv_id":"2302.01550","repositories_listed":0,"syntology":null},{"url":null,"slug":"convergence-of-gradient-descent-with-linearly","title":"Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy","date":"2023-02-02","arxiv_id":"2302.01463","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-analytics-a-survey","title":"Federated Analytics: A survey","date":"2023-02-02","arxiv_id":"2302.01326","repositories_listed":0,"syntology":null},{"url":null,"slug":"catfl-certificateless-authentication-based","title":"CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications","date":"2023-02-01","arxiv_id":"2302.00271","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-traffic-synthesis-and","title":"Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach","date":"2023-02-01","arxiv_id":"2302.00207","repositories_listed":0,"syntology":null},{"url":null,"slug":"flstra-federated-learning-in-stratosphere","title":"FLSTRA: Federated Learning in Stratosphere","date":"2023-02-01","arxiv_id":"2302.00163","repositories_listed":0,"syntology":null},{"url":null,"slug":"texttt-docofl-downlink-compression-for-cross","title":"DoCoFL: Downlink Compression for Cross-Device Federated Learning","date":"2023-02-01","arxiv_id":"2302.00543","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-sequential-federated-learning","title":"Distributed sequential federated learning","date":"2023-01-31","arxiv_id":"2302.00107","repositories_listed":0,"syntology":null},{"url":null,"slug":"truthful-incentive-mechanism-for-federated","title":"Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling","date":"2023-01-31","arxiv_id":"2302.00106","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-water-consumption","title":"Federated Learning for Water Consumption Forecasting in Smart Cities","date":"2023-01-30","arxiv_id":"2301.13036","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedpass-privacy-preserving-vertical-federated","title":"FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation","date":"2023-01-30","arxiv_id":"2301.12623","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fair-value-of-data-under-heterogeneous","title":"The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning","date":"2023-01-30","arxiv_id":"2301.13336","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedeba-towards-fair-and-effective-federated","title":"Entropy-driven Fair and Effective Federated Learning","date":"2023-01-29","arxiv_id":"2301.12407","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyclicfl-a-cyclic-model-pre-training-approach","title":"CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning","date":"2023-01-28","arxiv_id":"2301.12193","repositories_listed":0,"syntology":null},{"url":null,"slug":"splitgnn-splitting-gnn-for-node","title":"SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention","date":"2023-01-27","arxiv_id":"2301.12885","repositories_listed":0,"syntology":null},{"url":null,"slug":"uplink-scheduling-in-federated-learning-an","title":"Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning","date":"2023-01-27","arxiv_id":"2301.11903","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-over-coupled-graphs","title":"Federated Learning over Coupled Graphs","date":"2023-01-26","arxiv_id":"2301.11099","repositories_listed":0,"syntology":null},{"url":null,"slug":"interaction-level-membership-inference-attack","title":"Interaction-level Membership Inference Attack Against Federated Recommender Systems","date":"2023-01-26","arxiv_id":"2301.10964","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalised-federated-learning-on","title":"Personalised Federated Learning On Heterogeneous Feature Spaces","date":"2023-01-26","arxiv_id":"2301.11447","repositories_listed":0,"syntology":null},{"url":null,"slug":"superfed-weight-shared-federated-learning","title":"SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference","date":"2023-01-26","arxiv_id":"2301.10879","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-sensitive-learning-for-heterogeneous","title":"Time-sensitive Learning for Heterogeneous Federated Edge Intelligence","date":"2023-01-26","arxiv_id":"2301.10977","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-local-real-data-with-global","title":"Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning","date":"2023-01-25","arxiv_id":"2301.10394","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-to-trust-aggregated-gradients-addressing","title":"When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning","date":"2023-01-25","arxiv_id":"2301.10400","repositories_listed":0,"syntology":null},{"url":null,"slug":"polarair-a-compressed-sensing-scheme-for-over","title":"PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning","date":"2023-01-24","arxiv_id":"2301.10110","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-does-the-student-surpass-the-teacher","title":"When does the student surpass the teacher? Federated Semi-supervised Learning with Teacher-Student EMA","date":"2023-01-24","arxiv_id":"2301.10114","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-fair-federated-learning-adaptive","title":"Accelerating Fair Federated Learning: Adaptive Federated Adam","date":"2023-01-23","arxiv_id":"2301.09357","repositories_listed":0,"syntology":null},{"url":null,"slug":"backdoor-attacks-in-peer-to-peer-federated","title":"Backdoor Attacks in Peer-to-Peer Federated Learning","date":"2023-01-23","arxiv_id":"2301.09732","repositories_listed":0,"syntology":null},{"url":null,"slug":"baybfed-bayesian-backdoor-defense-for","title":"BayBFed: Bayesian Backdoor Defense for Federated Learning","date":"2023-01-23","arxiv_id":"2301.09508","repositories_listed":0,"syntology":null},{"url":null,"slug":"combined-use-of-federated-learning-and-image","title":"Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer","date":"2023-01-23","arxiv_id":"2301.09255","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-sufficient-dimension-reduction","title":"Federated Sufficient Dimension Reduction Through High-Dimensional Sparse Sliced Inverse Regression","date":"2023-01-23","arxiv_id":"2301.09500","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-communication-efficient-adaptive-algorithm","title":"A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret","date":"2023-01-21","arxiv_id":"2301.08869","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-potent-are-evasion-attacks-for-poisoning","title":"How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?","date":"2023-01-21","arxiv_id":"2301.08866","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-best-of-both-worlds-accurate-global-and","title":"The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation","date":"2023-01-21","arxiv_id":"2301.08968","repositories_listed":0,"syntology":null},{"url":null,"slug":"remote-patient-monitoring-using-artificial","title":"Remote patient monitoring using artificial intelligence: Current state, applications, and challenges","date":"2023-01-19","arxiv_id":"2301.10009","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-automatic-differentiation","title":"Federated Automatic Differentiation","date":"2023-01-18","arxiv_id":"2301.07806","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-knowledge-adaptation-for-federated","title":"Robust Knowledge Adaptation for Federated Unsupervised Person ReID","date":"2023-01-18","arxiv_id":"2301.07320","repositories_listed":0,"syntology":null},{"url":null,"slug":"convergence-of-first-order-algorithms-for","title":"Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes","date":"2023-01-17","arxiv_id":"2301.06806","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedclip-federated-learning-with-client","title":"FedCliP: Federated Learning with Client Pruning","date":"2023-01-17","arxiv_id":"2301.06768","repositories_listed":0,"syntology":null},{"url":null,"slug":"segviz-a-federated-learning-framework-for","title":"SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations","date":"2023-01-17","arxiv_id":"2301.07074","repositories_listed":0,"syntology":null},{"url":null,"slug":"hiflash-communication-efficient-hierarchical","title":"HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association","date":"2023-01-16","arxiv_id":"2301.06447","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedssc-shared-supervised-contrastive","title":"FedSSC: Shared Supervised-Contrastive Federated Learning","date":"2023-01-14","arxiv_id":"2301.05797","repositories_listed":0,"syntology":null},{"url":null,"slug":"poisoning-attacks-and-defenses-in-federated","title":"Poisoning Attacks and Defenses in Federated Learning: A Survey","date":"2023-01-14","arxiv_id":"2301.05795","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-transfer-ordered-personalized","title":"Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application","date":"2023-01-12","arxiv_id":"2301.04829","repositories_listed":0,"syntology":null},{"url":null,"slug":"jamming-attacks-on-decentralized-federated","title":"Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks","date":"2023-01-12","arxiv_id":"2301.05250","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-and-blockchain-enabled-fog","title":"Federated Learning and Blockchain-enabled Fog-IoT Platform for Wearables in Predictive Healthcare","date":"2023-01-11","arxiv_id":"2301.04511","repositories_listed":0,"syntology":null},{"url":null,"slug":"network-adaptive-federated-learning","title":"Network Adaptive Federated Learning: Congestion and Lossy Compression","date":"2023-01-11","arxiv_id":"2301.04430","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-energy-constrained-iot","title":"Federated Learning for Energy Constrained IoT devices: A systematic mapping study","date":"2023-01-09","arxiv_id":"2301.03720","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-federated-learning-a-practical-pet-yet","title":"Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation","date":"2023-01-09","arxiv_id":"2301.04017","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-randomized-block-coordinate","title":"Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problems","date":"2023-01-08","arxiv_id":"2301.03113","repositories_listed":0,"syntology":null},{"url":null,"slug":"anycostfl-efficient-on-demand-federated","title":"AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices","date":"2023-01-08","arxiv_id":"2301.03062","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-personalized-brain-functional","title":"Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks","date":"2023-01-06","arxiv_id":"2301.02423","repositories_listed":0,"syntology":null},{"url":null,"slug":"ms-dino-efficient-distributed-training-of","title":"Single-round Self-supervised Distributed Learning using Vision Transformer","date":"2023-01-05","arxiv_id":"2301.02064","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-system-identification-of-similar","title":"Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems","date":"2023-01-04","arxiv_id":"2301.01430","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-machine-learning-for-uav-swarms","title":"Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics","date":"2023-01-03","arxiv_id":"2301.00912","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-for-large-scale-optimization","title":"Machine Learning for Large-Scale Optimization in 6G Wireless Networks","date":"2023-01-03","arxiv_id":"2301.03377","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-advances-on-federated-learning-a","title":"Recent Advances on Federated Learning: A Systematic Survey","date":"2023-01-03","arxiv_id":"2301.01299","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-channel-sparsity-for-federated","title":"Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bias-eliminating-augmentation-learning-for","title":"Bias-Eliminating Augmentation Learning for Debiased Federated Learning","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-privacy-preservation-in-federated","title":"Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-and-effectiveness-in-federated","title":"Fairness and Effectiveness in Federated Learning on Non-independent and Identically Distributed Data","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fedpd-federated-open-set-recognition-with","title":"FedPD: Federated Open Set Recognition with Parameter Disentanglement","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fedseg-class-heterogeneous-federated-learning","title":"FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-prevent-the-poor-performance-clients","title":"How To Prevent the Poor Performance Clients for Personalized Federated Learning?","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mutual-information-regularization-for","title":"Mutual Information Regularization for Vertical Federated Learning","date":"2023-01-01","arxiv_id":"2301.01142","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-semantics-excitation-for","title":"Personalized Semantics Excitation for Federated Image Classification","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"reliable-and-interpretable-personalized","title":"Reliable and Interpretable Personalized Federated Learning","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spefl-efficient-security-and-privacy-enhanced","title":"SPEFL: Efficient Security and Privacy Enhanced Federated Learning Against Poisoning Attacks","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"workie-talkie-accelerating-federated-learning","title":"Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-hierarchy-quantization-compression","title":"Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling","date":"2022-12-30","arxiv_id":"2212.14760","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterization-of-the-global-bias-problem","title":"Characterization of the Global Bias Problem in Aerial Federated Learning","date":"2022-12-29","arxiv_id":"2212.14360","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-multi-agent-deep-reinforcement","title":"Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management","date":"2022-12-29","arxiv_id":"2301.00641","repositories_listed":0,"syntology":null},{"url":null,"slug":"ccfl-computationally-customized-federated","title":"CC-FedAvg: Computationally Customized Federated Averaging","date":"2022-12-28","arxiv_id":"2212.13679","repositories_listed":0,"syntology":null},{"url":null,"slug":"proof-of-swarm-based-ensemble-learning-for","title":"Proof of Swarm Based Ensemble Learning for Federated Learning Applications","date":"2022-12-28","arxiv_id":"2212.14050","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-federated-recommendation-systems","title":"A Survey on Federated Recommendation Systems","date":"2022-12-27","arxiv_id":"2301.00767","repositories_listed":0,"syntology":null},{"url":null,"slug":"democratising-knowledge-representation-with","title":"Democratising Knowledge Representation with BioCypher","date":"2022-12-27","arxiv_id":"2212.13543","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-guided-data-centric-ai-in","title":"Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions","date":"2022-12-27","arxiv_id":"2212.13591","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-aware-clustered-federated-learning","title":"Social-Aware Clustered Federated Learning with Customized Privacy Preservation","date":"2022-12-25","arxiv_id":"2212.13992","repositories_listed":0,"syntology":null}],"record_sha256":"901dcb52488244fd719eb8ffd1c25e762ed1c3f74156cbc31ad42a776b0b96b4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}