{"total":118,"rows":[{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"id":"2111.00364","title":"Sustainable AI: Environmental Implications, Challenges and Opportunities","created":"2021-10-30","cited_by_count":242,"area":null},{"id":"2102.06387","title":"The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation","created":"2021-02-12","cited_by_count":128,"area":null},{"id":"2203.11473","title":"Federated Class-Incremental Learning","created":"2022-03-22","cited_by_count":127,"area":null},{"id":"2104.08815","title":"FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks","created":"2021-04-18","cited_by_count":85,"area":null},{"id":"2110.04995","title":"The Skellam Mechanism for Differentially Private Federated Learning","created":"2021-10-11","cited_by_count":68,"area":null},{"id":"2202.08827","title":"LAMP: Extracting Text from Gradients with Language Model Priors","created":"2022-02-17","cited_by_count":54,"area":null},{"id":"2201.12675","title":"Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models","created":"2022-01-29","cited_by_count":53,"area":null},{"id":"2302.00903","title":"No One Left Behind: Real-World Federated Class-Incremental Learning","created":"2023-02-02","cited_by_count":53,"area":null},{"id":"2107.12603","title":"Federated Learning Meets Natural Language Processing: A Survey","created":"2021-07-27","cited_by_count":43,"area":null},{"id":"2107.10996","title":"Communication Efficiency in Federated Learning: Achievements and Challenges","created":"2021-07-23","cited_by_count":40,"area":null},{"id":"2206.02291","title":"Pretrained Models for Multilingual Federated Learning","created":"2022-06-06","cited_by_count":30,"area":null},{"id":"2206.04621","title":"A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning","created":"2022-06-09","cited_by_count":27,"area":null},{"id":"2104.03008","title":"FedFace: Collaborative Learning of Face Recognition Model","created":"2021-04-07","cited_by_count":26,"area":null},{"id":"2107.03311","title":"RoFL: Robustness of Secure Federated Learning","created":"2021-07-07","cited_by_count":25,"area":null},{"id":"2111.05326","title":"The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning","created":"2021-11-09","cited_by_count":23,"area":null},{"id":"2108.02741","title":"GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning","created":"2021-08-05","cited_by_count":21,"area":null},{"id":"2301.01824","title":"Privacy and Efficiency of Communications in Federated Split Learning","created":"2023-01-04","cited_by_count":21,"area":null},{"id":"2306.04959","title":"FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs","created":"2023-06-08","cited_by_count":21,"area":null},{"id":"2409.11585","title":"Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework","created":"2024-09-17","cited_by_count":19,"area":null},{"id":"2101.03705","title":"FedAR: Activity and Resource-Aware Federated Learning Model for Distributed Mobile Robots","created":"2021-01-11","cited_by_count":18,"area":null},{"id":"2302.12862","title":"FLINT: A Platform for Federated Learning Integration","created":"2023-02-24","cited_by_count":17,"area":null},{"id":"2207.08988","title":"Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices","created":"2022-07-18","cited_by_count":16,"area":null},{"id":"2103.11879","title":"Real-time End-to-End Federated Learning: An Automotive Case Study","created":"2021-03-22","cited_by_count":15,"area":null},{"id":"2410.08892","title":"Federated Learning in Practice: Reflections and Projections","created":"2024-10-11","cited_by_count":15,"area":null},{"id":"2112.13097","title":"Faster Rates for Compressed Federated Learning with Client-Variance Reduction","created":"2021-12-24","cited_by_count":14,"area":null},{"id":"2312.05842","title":"Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer","created":"2023-12-10","cited_by_count":13,"area":null},{"id":"2411.00329","title":"Personalized Federated Learning via Feature Distribution Adaptation","created":"2024-11-01","cited_by_count":13,"area":null},{"id":"2110.03634","title":"Enabling On-Device Training of Speech Recognition Models with Federated Dropout","created":"2021-10-07","cited_by_count":12,"area":null},{"id":"2403.11460","title":"Fed3DGS: Scalable 3D Gaussian Splatting with Federated Learning","created":"2024-03-18","cited_by_count":12,"area":null},{"id":"2101.11896","title":"Self-supervised Cross-silo Federated Neural Architecture Search","created":"2021-01-28","cited_by_count":11,"area":null},{"id":"2103.13266","title":"Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications","created":"2021-03-24","cited_by_count":11,"area":null},{"id":"2202.03099","title":"FL_PyTorch: optimization research simulator for federated learning","created":"2022-02-07","cited_by_count":10,"area":null},{"id":"2406.06755","title":"Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints","created":"2024-06-10","cited_by_count":10,"area":null},{"id":"2106.04384","title":"FL-Market: Trading Private Models in Federated Learning","created":"2021-06-08","cited_by_count":9,"area":null},{"id":"2109.03999","title":"Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies","created":"2021-09-09","cited_by_count":9,"area":null},{"id":"2308.12210","title":"ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy","created":"2023-08-23","cited_by_count":9,"area":null},{"id":"2403.04784","title":"Analysis of Privacy Leakage in Federated Large Language Models","created":"2024-03-02","cited_by_count":9,"area":null},{"id":"2406.12815","title":"Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation","created":"2024-06-18","cited_by_count":9,"area":null},{"id":"2409.17446","title":"Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability","created":"2024-09-26","cited_by_count":9,"area":null},{"id":"2205.13655","title":"Mixed Federated Learning: Joint Decentralized and Centralized Learning","created":"2022-05-26","cited_by_count":8,"area":null},{"id":"2206.09264","title":"Pisces: Efficient Federated Learning via Guided Asynchronous Training","created":"2022-06-18","cited_by_count":8,"area":null},{"id":"2208.09894","title":"Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning","created":"2022-08-21","cited_by_count":8,"area":null},{"id":"2101.00159","title":"Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning","created":"2021-01-01","cited_by_count":7,"area":null},{"id":"2111.12150","title":"Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift","created":"2021-11-23","cited_by_count":7,"area":null},{"id":"2404.09232","title":"MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes","created":"2024-04-14","cited_by_count":7,"area":null},{"id":"2203.15966","title":"Federated Domain Adaptation for ASR with Full Self-Supervision","created":"2022-03-30","cited_by_count":6,"area":null},{"id":"2305.07772","title":"Monitoring and Adapting ML Models on Mobile Devices","created":"2023-05-12","cited_by_count":6,"area":null},{"id":"2312.05720","title":"Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning","created":"2023-12-10","cited_by_count":6,"area":null},{"id":"2406.06749","title":"Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests","created":"2024-06-10","cited_by_count":6,"area":null},{"id":"2107.06877","title":"Federated Self-Training for Semi-Supervised Audio Recognition","created":"2021-07-14","cited_by_count":5,"area":null},{"id":"2201.06244","title":"EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party","created":"2022-01-17","cited_by_count":5,"area":null},{"id":"2211.06401","title":"A Federated Approach to Predicting Emojis in Hindi Tweets","created":"2022-11-11","cited_by_count":5,"area":null},{"id":"2402.12265","title":"On the Byzantine-Resilience of Distillation-Based Federated Learning","created":"2024-02-19","cited_by_count":5,"area":"distill"},{"id":"2107.11954","title":"Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes","created":"2021-07-26","cited_by_count":4,"area":null},{"id":"2108.09081","title":"FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update","created":"2021-08-20","cited_by_count":4,"area":null},{"id":"2201.02582","title":"Multi-Model Federated Learning","created":"2022-01-07","cited_by_count":4,"area":null},{"id":"2202.07165","title":"OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsification","created":"2022-02-15","cited_by_count":4,"area":null},{"id":"2202.09848","title":"Personalized Federated Learning with Exact Stochastic Gradient Descent","created":"2022-02-20","cited_by_count":4,"area":null},{"id":"2205.03092","title":"Federated Learning with Noisy User Feedback","created":"2022-05-06","cited_by_count":4,"area":null},{"id":"2211.01588","title":"A Convergence Theory for Federated Average: Beyond Smoothness","created":"2022-11-03","cited_by_count":4,"area":null},{"id":"2404.10764","title":"Confidential Federated Computations","created":"2024-04-16","cited_by_count":4,"area":null},{"id":"2110.00155","title":"Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device","created":"2021-10-01","cited_by_count":3,"area":null},{"id":"2207.12327","title":"Technical Report: Assisting Backdoor Federated Learning with Whole Population Knowledge Alignment","created":"2022-07-25","cited_by_count":3,"area":null},{"id":"2304.01005","title":"Federated Learning Based Multilingual Emoji Prediction In Clean and Attack Scenarios","created":"2023-03-30","cited_by_count":3,"area":null},{"id":"2307.02140","title":"Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives","created":"2023-07-05","cited_by_count":3,"area":null},{"id":"2311.04944","title":"Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things","created":"2023-11-08","cited_by_count":3,"area":null},{"id":"2401.01168","title":"FedQV: Leveraging Quadratic Voting in Federated Learning","created":"2024-01-02","cited_by_count":3,"area":null},{"id":"2406.02416","title":"Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials","created":"2024-06-04","cited_by_count":3,"area":null},{"id":"2410.08027","title":"Private Language Models via Truncated Laplacian Mechanism","created":"2024-10-10","cited_by_count":3,"area":null},{"id":"2106.14265","title":"Reward-Based 1-bit Compressed Federated Distillation on Blockchain","created":"2021-06-27","cited_by_count":2,"area":"distill"},{"id":"2108.07433","title":"Aggregation Delayed Federated Learning","created":"2021-08-17","cited_by_count":2,"area":null},{"id":"2112.12028","title":"VoiceMoji: A Novel On-Device Pipeline for Seamless Emoji Insertion in Dictation","created":"2021-12-22","cited_by_count":2,"area":null},{"id":"2202.01529","title":"Comparative assessment of federated and centralized machine learning","created":"2022-02-03","cited_by_count":2,"area":null},{"id":"2205.10848","title":"Robust Quantity-Aware Aggregation for Federated Learning","created":"2022-05-22","cited_by_count":2,"area":null},{"id":"2207.11788","title":"Privacy Against Inference Attacks in Vertical Federated Learning","created":"2022-07-24","cited_by_count":2,"area":null},{"id":"2301.07806","title":"Federated Automatic Differentiation","created":"2023-01-18","cited_by_count":2,"area":null},{"id":"2303.10218","title":"An Empirical Evaluation of Federated Contextual Bandit Algorithms","created":"2023-03-17","cited_by_count":2,"area":null},{"id":"2308.03320","title":"Binary Federated Learning with Client-Level Differential Privacy","created":"2023-08-07","cited_by_count":2,"area":null},{"id":"2310.04055","title":"Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification","created":"2023-10-06","cited_by_count":2,"area":"safety"},{"id":"2406.01115","title":"Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning","created":"2024-06-03","cited_by_count":2,"area":null},{"id":"2409.03897","title":"On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments","created":"2024-09-05","cited_by_count":2,"area":"deeprl"},{"id":"2409.06123","title":"Contrastive Federated Learning with Tabular Data Silos","created":"2024-09-10","cited_by_count":2,"area":null},{"id":"2411.05031","title":"On-Device Emoji Classifier Trained with GPT-based Data Augmentation for a Mobile Keyboard","created":"2024-11-06","cited_by_count":2,"area":null},{"id":"2603.00516","title":"ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Data","created":"2026-02-28","cited_by_count":2,"area":"safety"},{"id":"2103.07704","title":"Simeon -- Secure Federated Machine Learning Through Iterative Filtering","created":"2021-03-13","cited_by_count":1,"area":null},{"id":"2103.11226","title":"Demystifying the Effects of Non-Independence in Federated Learning","created":"2021-03-20","cited_by_count":1,"area":null},{"id":"2107.11956","title":"Preliminary Steps Towards Federated Sentiment Classification","created":"2021-07-26","cited_by_count":1,"area":null},{"id":"2207.04202","title":"Smart Multi-tenant Federated Learning","created":"2022-07-09","cited_by_count":1,"area":null},{"id":"2208.12959","title":"Tensor Decomposition based Personalized Federated Learning","created":"2022-08-27","cited_by_count":1,"area":null},{"id":"2210.01863","title":"Group Personalized Federated Learning","created":"2022-10-04","cited_by_count":1,"area":null},{"id":"2211.16634","title":"SPARTAN: Sparse Hierarchical Memory for Parameter-Efficient Transformers","created":"2022-11-29","cited_by_count":1,"area":null},{"id":"2302.05545","title":"Privacy Against Agnostic Inference Attacks in Vertical Federated Learning","created":"2023-02-10","cited_by_count":1,"area":null},{"id":"2302.09243","title":"A Federated Approach for Hate Speech Detection","created":"2023-02-18","cited_by_count":1,"area":null},{"id":"2305.19600","title":"Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning","created":"2023-05-31","cited_by_count":1,"area":"distill"},{"id":"2306.03600","title":"Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations","created":"2023-06-06","cited_by_count":1,"area":"safety"},{"id":"2311.05826","title":"Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios","created":"2023-11-10","cited_by_count":1,"area":"safety"},{"id":"2312.08298","title":"Venn: Resource Management for Collaborative Learning Jobs","created":"2023-12-13","cited_by_count":1,"area":null},{"id":"2405.02360","title":"Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning","created":"2024-05-03","cited_by_count":1,"area":null},{"id":"2405.14291","title":"Variational Bayes for Federated Continual Learning","created":"2024-05-23","cited_by_count":1,"area":null}],"next_cursor":"100"}