{"total":32,"rows":[{"id":"2104.02151","title":"Distributed Learning in Wireless Networks: Recent Progress and Future Challenges","created":"2021-04-05","cited_by_count":129,"area":null},{"id":"2104.11375","title":"Decentralized Federated Averaging","created":"2021-04-23","cited_by_count":114,"area":null},{"id":"2102.02888","title":"1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed","created":"2021-02-04","cited_by_count":75,"area":null},{"id":"2102.04487","title":"Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning","created":"2021-02-08","cited_by_count":63,"area":null},{"id":"2202.00998","title":"3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation","created":"2022-02-02","cited_by_count":27,"area":null},{"id":"2104.06069","title":"1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed","created":"2021-04-13","cited_by_count":24,"area":null},{"id":"2206.08829","title":"FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning","created":"2022-06-17","cited_by_count":23,"area":null},{"id":"2210.16402","title":"GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity","created":"2022-10-28","cited_by_count":18,"area":null},{"id":"2211.00188","title":"Adaptive Compression for Communication-Efficient Distributed Training","created":"2022-10-31","cited_by_count":16,"area":null},{"id":"2210.07588","title":"Distributed Distributionally Robust Optimization with Non-Convex Objectives","created":"2022-10-14","cited_by_count":14,"area":null},{"id":"2104.06685","title":"BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning","created":"2021-04-14","cited_by_count":13,"area":null},{"id":"2206.03588","title":"Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation","created":"2022-06-07","cited_by_count":11,"area":null},{"id":"2411.19870","title":"DeMo: Decoupled Momentum Optimization","created":"2024-11-29","cited_by_count":11,"area":null},{"id":"2211.02212","title":"Distributed Linear Bandits under Communication Constraints","created":"2022-11-04","cited_by_count":8,"area":null},{"id":"2110.07751","title":"Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation","created":"2021-10-14","cited_by_count":7,"area":null},{"id":"2301.08869","title":"A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret","created":"2023-01-21","cited_by_count":7,"area":null},{"id":"2205.11044","title":"Personalized Federated Learning with Server-Side Information","created":"2022-05-23","cited_by_count":6,"area":null},{"id":"2307.10492","title":"Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior","created":"2023-07-19","cited_by_count":5,"area":null},{"id":"2406.18156","title":"FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization","created":"2024-06-26","cited_by_count":5,"area":null},{"id":"2204.07773","title":"FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning","created":"2022-04-16","cited_by_count":4,"area":null},{"id":"2307.14364","title":"Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis","created":"2023-07-25","cited_by_count":4,"area":null},{"id":"2403.14716","title":"Distributed Learning based on 1-Bit Gradient Coding in the Presence of Stragglers","created":"2024-03-19","cited_by_count":4,"area":null},{"id":"2211.04152","title":"Federated Learning Using Three-Operator ADMM","created":"2022-11-08","cited_by_count":3,"area":null},{"id":"2404.15943","title":"Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme","created":"2024-04-24","cited_by_count":3,"area":null},{"id":"2206.07126","title":"Lazy Queries Can Reduce Variance in Zeroth-order Optimization","created":"2022-06-14","cited_by_count":2,"area":null},{"id":"2309.06805","title":"FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization","created":"2023-09-13","cited_by_count":2,"area":null},{"id":"2404.13348","title":"Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems","created":"2024-04-20","cited_by_count":2,"area":null},{"id":"2304.00737","title":"SparDL: Distributed Deep Learning Training with Efficient Sparse Communication","created":"2023-04-03","cited_by_count":0,"area":null},{"id":"2306.05655","title":"Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications","created":"2023-06-09","cited_by_count":0,"area":null},{"id":"2309.10834","title":"Communication-Efficient Federated Learning via Regularized Sparse Random Networks","created":"2023-09-19","cited_by_count":0,"area":null},{"id":"2408.08430","title":"Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning","created":"2024-08-15","cited_by_count":0,"area":null},{"id":"2501.05633","title":"Regularized Top-$k$: A Bayesian Framework for Gradient Sparsification","created":"2025-01-10","cited_by_count":0,"area":null}],"next_cursor":null}