{"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/36","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":36,"pages_in_order":68,"rows_per_page":100,"rows":[3501,3600],"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/35","next":"/task/federated-learning/papers/37","papers":[{"url":null,"slug":"differentially-private-online-federated","title":"Differentially Private Online Federated Learning with Correlated Noise","date":"2024-03-25","arxiv_id":"2403.16542","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-collaborative-anomalous-sound","title":"Distributed collaborative anomalous sound detection by embedding sharing","date":"2024-03-25","arxiv_id":"2403.16610","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedac-a-adaptive-clustered-federated-learning","title":"FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data","date":"2024-03-25","arxiv_id":"2403.16460","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedfixer-mitigating-heterogeneous-label-noise","title":"FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning","date":"2024-03-25","arxiv_id":"2403.16561","repositories_listed":0,"syntology":null},{"url":null,"slug":"fligan-enhancing-federated-learning-with","title":"FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN","date":"2024-03-25","arxiv_id":"2403.16930","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-the-representation-in-federated","title":"Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data","date":"2024-03-25","arxiv_id":"2403.16398","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-federated-parameter-aggregation-method-for","title":"A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures","date":"2024-03-24","arxiv_id":"2403.16004","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-and-efficient-federated-split","title":"Heterogeneous Federated Learning with Splited Language Model","date":"2024-03-24","arxiv_id":"2403.16050","repositories_listed":0,"syntology":null},{"url":null,"slug":"initialisation-and-topology-effects-in","title":"Initialisation and Network Effects in Decentralised Federated Learning","date":"2024-03-23","arxiv_id":"2403.15855","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-coded-federated-learning-privacy","title":"Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation","date":"2024-03-22","arxiv_id":"2403.14905","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficiently-assemble-normalization-layers-and","title":"Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization","date":"2024-03-22","arxiv_id":"2403.15605","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-bayesian-deep-learning-the","title":"Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models","date":"2024-03-22","arxiv_id":"2403.15263","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-iiot-with-over-the-air-federated","title":"Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruning","date":"2024-03-21","arxiv_id":"2403.14120","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedmef-towards-memory-efficient-federated","title":"FedMef: Towards Memory-efficient Federated Dynamic Pruning","date":"2024-03-21","arxiv_id":"2403.14737","repositories_listed":0,"syntology":null},{"url":null,"slug":"byzantine-resilient-federated-learning-with","title":"Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity","date":"2024-03-20","arxiv_id":"2403.13374","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralized-federated-learning-model-update","title":"FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis","date":"2024-03-20","arxiv_id":"2403.13247","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-feature-communication-in-federated","title":"Leveraging feature communication in federated learning for remote sensing image classification","date":"2024-03-20","arxiv_id":"2403.13575","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptsfl-adaptive-split-federated-learning-in","title":"AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks","date":"2024-03-19","arxiv_id":"2403.13101","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-the-impact-of-partial-sharing-on","title":"Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing","date":"2024-03-19","arxiv_id":"2403.13108","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-semi-supervised-learning-for","title":"Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency","date":"2024-03-19","arxiv_id":"2403.12695","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedsr-a-semi-decentralized-federated-learning","title":"FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT System","date":"2024-03-19","arxiv_id":"2403.14718","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-lora-in-privacy-preserving","title":"Improving LoRA in Privacy-preserving Federated Learning","date":"2024-03-18","arxiv_id":"2403.12313","repositories_listed":0,"syntology":null},{"url":null,"slug":"knfu-effective-knowledge-fusion","title":"KnFu: Effective Knowledge Fusion","date":"2024-03-18","arxiv_id":"2403.11892","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-transfer-learning-with-differential","title":"Federated Transfer Learning with Differential Privacy","date":"2024-03-17","arxiv_id":"2403.11343","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-iot-security-against-ddos-attacks","title":"Enhancing IoT Security Against DDoS Attacks through Federated Learning","date":"2024-03-16","arxiv_id":"2403.10968","repositories_listed":0,"syntology":null},{"url":null,"slug":"fagh-accelerating-federated-learning-with","title":"FAGH: Accelerating Federated Learning with Approximated Global Hessian","date":"2024-03-16","arxiv_id":"2403.11041","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-aggregation-with-latent-generative","title":"Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours","date":"2024-03-16","arxiv_id":"2405.15773","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedqnn-federated-learning-using-quantum","title":"FedQNN: Federated Learning using Quantum Neural Networks","date":"2024-03-16","arxiv_id":"2403.10861","repositories_listed":0,"syntology":null},{"url":null,"slug":"da-pfl-dynamic-affinity-aggregation-for","title":"DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning","date":"2024-03-14","arxiv_id":"2403.09284","repositories_listed":0,"syntology":null},{"url":null,"slug":"defense-via-behavior-attestation-against","title":"Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems","date":"2024-03-14","arxiv_id":"2403.09531","repositories_listed":0,"syntology":null},{"url":null,"slug":"empowering-healthcare-through-privacy","title":"Empowering Healthcare through Privacy-Preserving MRI Analysis","date":"2024-03-14","arxiv_id":"2403.09836","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-machine-learning-based-security","title":"Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems","date":"2024-03-14","arxiv_id":"2403.09752","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-aware-multi-server-federated","title":"Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks","date":"2024-03-14","arxiv_id":"2403.09153","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedcomloc-communication-efficient-distributed","title":"FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models","date":"2024-03-14","arxiv_id":"2403.09904","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-straggler-clients-in-federated","title":"Learning from straggler clients in federated learning","date":"2024-03-14","arxiv_id":"2403.09086","repositories_listed":0,"syntology":null},{"url":null,"slug":"metadata-driven-federated-learning-of","title":"Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios","date":"2024-03-14","arxiv_id":"2403.09139","repositories_listed":0,"syntology":null},{"url":null,"slug":"taming-cross-domain-representation-variance","title":"Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains","date":"2024-03-14","arxiv_id":"2403.09048","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupled-federated-learning-on-long-tailed","title":"Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics","date":"2024-03-13","arxiv_id":"2403.08364","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-knowledge-graph-unlearning-via","title":"Federated Knowledge Graph Unlearning via Diffusion Model","date":"2024-03-13","arxiv_id":"2403.08554","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgic-a-multi-label-gradient-inversion-attack","title":"MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning","date":"2024-03-13","arxiv_id":"2403.08284","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-language-model-architectures-for","title":"Efficient Language Model Architectures for Differentially Private Federated Learning","date":"2024-03-12","arxiv_id":"2403.08100","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-federated-learning-over-the-air","title":"Adaptive Federated Learning Over the Air","date":"2024-03-11","arxiv_id":"2403.06528","repositories_listed":0,"syntology":null},{"url":null,"slug":"diprompt-disentangled-prompt-tuning-for","title":"DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning","date":"2024-03-11","arxiv_id":"2403.08506","repositories_listed":0,"syntology":null},{"url":null,"slug":"don-t-forget-what-i-did-assessing-client","title":"Don't Forget What I did?: Assessing Client Contributions in Federated Learning","date":"2024-03-11","arxiv_id":"2403.07151","repositories_listed":0,"syntology":null},{"url":null,"slug":"provable-mutual-benefits-from-federated","title":"Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains","date":"2024-03-11","arxiv_id":"2403.06672","repositories_listed":0,"syntology":null},{"url":null,"slug":"uav-enabled-asynchronous-federated-learning","title":"UAV-Enabled Asynchronous Federated Learning","date":"2024-03-11","arxiv_id":"2403.06653","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-total-variation-minimization-for","title":"Analysis of Total Variation Minimization for Clustered Federated Learning","date":"2024-03-10","arxiv_id":"2403.06298","repositories_listed":0,"syntology":null},{"url":null,"slug":"fake-or-compromised-making-sense-of-malicious","title":"Fake or Compromised? Making Sense of Malicious Clients in Federated Learning","date":"2024-03-10","arxiv_id":"2403.06319","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedpit-towards-privacy-preserving-and-few","title":"FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning","date":"2024-03-10","arxiv_id":"2403.06131","repositories_listed":0,"syntology":null},{"url":null,"slug":"fluent-round-efficient-secure-aggregation-for","title":"Fluent: Round-efficient Secure Aggregation for Private Federated Learning","date":"2024-03-10","arxiv_id":"2403.06143","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-efficient-replay-in-federated","title":"Towards Efficient Replay in Federated Incremental Learning","date":"2024-03-09","arxiv_id":"2403.05890","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-method-for-preserving","title":"Federated Learning Method for Preserving Privacy in Face Recognition System","date":"2024-03-08","arxiv_id":"2403.05344","repositories_listed":0,"syntology":null},{"url":null,"slug":"ris-empowered-topology-control-for","title":"RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility","date":"2024-03-08","arxiv_id":"2403.05133","repositories_listed":0,"syntology":null},{"url":null,"slug":"architectural-blueprint-for-heterogeneity","title":"Architectural Blueprint For Heterogeneity-Resilient Federated Learning","date":"2024-03-07","arxiv_id":"2403.04546","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-fairness-and-robustness-in-over-the","title":"Boosting Fairness and Robustness in Over-the-Air Federated Learning","date":"2024-03-07","arxiv_id":"2403.04431","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedclust-optimizing-federated-learning-on-non","title":"FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering","date":"2024-03-07","arxiv_id":"2403.04144","repositories_listed":0,"syntology":null},{"url":null,"slug":"fl-guard-a-holistic-framework-for-run-time","title":"FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning","date":"2024-03-07","arxiv_id":"2403.04146","repositories_listed":0,"syntology":null},{"url":null,"slug":"locodl-communication-efficient-distributed","title":"LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression","date":"2024-03-07","arxiv_id":"2403.04348","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-demand-quantization-for-green-federated","title":"On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks","date":"2024-03-07","arxiv_id":"2403.04430","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupled-vertical-federated-learning-for","title":"Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data","date":"2024-03-06","arxiv_id":"2403.03871","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-you-trust-your-model-emerging-malware","title":"Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem","date":"2024-03-06","arxiv_id":"2403.03593","repositories_listed":0,"syntology":null},{"url":null,"slug":"many-objective-multi-solution-transport","title":"Many-Objective Multi-Solution Transport","date":"2024-03-06","arxiv_id":"2403.04099","repositories_listed":0,"syntology":null},{"url":null,"slug":"ocd-fl-a-novel-communication-efficient-peer","title":"OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning","date":"2024-03-06","arxiv_id":"2403.04037","repositories_listed":0,"syntology":null},{"url":null,"slug":"spear-exact-gradient-inversion-of-batches-in","title":"SPEAR:Exact Gradient Inversion of Batches in Federated Learning","date":"2024-03-06","arxiv_id":"2403.03945","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-security-in-federated-learning","title":"Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation","date":"2024-03-05","arxiv_id":"2403.04803","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-federated-learning-and-edge","title":"Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks","date":"2024-03-05","arxiv_id":"2403.03165","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-federated-learning-for-automatic","title":"Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures","date":"2024-03-05","arxiv_id":"2403.03368","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-aware-semantic-cache-for-large","title":"MeanCache: User-Centric Semantic Caching for LLM Web Services","date":"2024-03-05","arxiv_id":"2403.02694","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-clustered-federated-learning-in","title":"Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks","date":"2024-03-05","arxiv_id":"2403.03157","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-federated-learning-mitigates-client","title":"Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks","date":"2024-03-05","arxiv_id":"2403.03149","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-federated-learning-via-logits","title":"Towards Robust Federated Learning via Logits Calibration on Non-IID Data","date":"2024-03-05","arxiv_id":"2403.02803","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-machine-learning-models-at-the-edge","title":"Training Machine Learning models at the Edge: A Survey","date":"2024-03-05","arxiv_id":"2403.02619","repositories_listed":0,"syntology":null},{"url":null,"slug":"sok-challenges-and-opportunities-in-federated","title":"A Survey on Federated Unlearning: Challenges and Opportunities","date":"2024-03-04","arxiv_id":"2403.02437","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-optimal-customized-architecture-for","title":"Towards Optimal Customized Architecture for Heterogeneous Federated Learning with Contrastive Cloud-Edge Model Decoupling","date":"2024-03-04","arxiv_id":"2403.02360","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-of-federated-transfer","title":"A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications","date":"2024-03-03","arxiv_id":"2403.01387","repositories_listed":0,"syntology":null},{"url":null,"slug":"asyn2f-an-asynchronous-federated-learning","title":"Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation","date":"2024-03-03","arxiv_id":"2403.01417","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-data-provenance-and-model","title":"Enhancing Data Provenance and Model Transparency in Federated Learning Systems - A Database Approach","date":"2024-03-03","arxiv_id":"2403.01451","repositories_listed":0,"syntology":null},{"url":null,"slug":"partial-federated-learning","title":"Partial Federated Learning","date":"2024-03-03","arxiv_id":"2403.01615","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantized-hierarchical-federated-learning-a","title":"A Hierarchical Federated Learning Approach for the Internet of Things","date":"2024-03-03","arxiv_id":"2403.01540","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-speech-recognition-using-advanced","title":"Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey","date":"2024-03-02","arxiv_id":"2403.01255","repositories_listed":0,"syntology":null},{"url":null,"slug":"defending-against-data-reconstruction-attacks","title":"Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach","date":"2024-03-02","arxiv_id":"2403.01268","repositories_listed":0,"syntology":null},{"url":null,"slug":"cloud-based-federated-learning-framework-for","title":"Cloud-based Federated Learning Framework for MRI Segmentation","date":"2024-03-01","arxiv_id":"2403.00254","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-via-lattice-joint-source","title":"Federated Learning via Lattice Joint Source-Channel Coding","date":"2024-03-01","arxiv_id":"2403.01023","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedrdma-communication-efficient-cross-silo","title":"FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission","date":"2024-03-01","arxiv_id":"2403.00881","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-linear-contextual-bandits-with","title":"Federated Linear Contextual Bandits with Heterogeneous Clients","date":"2024-02-29","arxiv_id":"2403.00116","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-group-connectivity-for","title":"Improving Group Connectivity for Generalization of Federated Deep Learning","date":"2024-02-29","arxiv_id":"2402.18949","repositories_listed":0,"syntology":null},{"url":null,"slug":"privateyes-appearance-based-gaze-estimation","title":"PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation","date":"2024-02-29","arxiv_id":"2402.18970","repositories_listed":0,"syntology":null},{"url":null,"slug":"robwe-robust-watermark-embedding-for","title":"RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection","date":"2024-02-29","arxiv_id":"2402.19054","repositories_listed":0,"syntology":null},{"url":null,"slug":"sprifed-omp-a-differentially-private","title":"SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery","date":"2024-02-29","arxiv_id":"2402.19016","repositories_listed":0,"syntology":null},{"url":null,"slug":"auditable-homomorphic-based-decentralized","title":"Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy","date":"2024-02-28","arxiv_id":"2403.00023","repositories_listed":0,"syntology":null},{"url":null,"slug":"communication-efficient-confederated-learning","title":"Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach","date":"2024-02-28","arxiv_id":"2402.18018","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralised-traffic-incident-detection-via","title":"Decentralised Traffic Incident Detection via Network Lasso","date":"2024-02-28","arxiv_id":"2402.18167","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-network-topology-on-the-performance","title":"Impact of network topology on the performance of Decentralized Federated Learning","date":"2024-02-28","arxiv_id":"2402.18606","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedbrb-an-effective-solution-to-the-small-to","title":"FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning","date":"2024-02-27","arxiv_id":"2402.17202","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-estimating","title":"Federated Learning for Estimating Heterogeneous Treatment Effects","date":"2024-02-27","arxiv_id":"2402.17705","repositories_listed":0,"syntology":null},{"url":null,"slug":"feduv-uniformity-and-variance-for","title":"FedUV: Uniformity and Variance for Heterogeneous Federated Learning","date":"2024-02-27","arxiv_id":"2402.18372","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralized-federated-unlearning-on","title":"BlockFUL: Enabling Unlearning in Blockchained Federated Learning","date":"2024-02-26","arxiv_id":"2402.16294","repositories_listed":0,"syntology":null},{"url":null,"slug":"fedreview-a-review-mechanism-for-rejecting","title":"FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning","date":"2024-02-26","arxiv_id":"2402.16934","repositories_listed":0,"syntology":null},{"url":null,"slug":"mip-clip-based-image-reconstruction-from-peft","title":"MIP: CLIP-based Image Reconstruction from PEFT Gradients","date":"2024-02-26","arxiv_id":"2403.07901","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-access-in-the-era-of-distributed","title":"Multiple Access in the Era of Distributed Computing and Edge Intelligence","date":"2024-02-26","arxiv_id":"2403.07903","repositories_listed":0,"syntology":null}],"record_sha256":"c4e493f9905f9c4d056450f975f512da24fb75914ad52c70d818eb9e1dd96a6d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}