{"total":135,"rows":[{"id":"2403.03507","title":"GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection","created":"2024-03-06","cited_by_count":463,"area":null},{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"id":"2102.13451","title":"FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout","created":"2021-02-26","cited_by_count":192,"area":null},{"id":"2111.12444","title":"Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications","created":"2021-11-24","cited_by_count":97,"area":null},{"id":"2106.09352","title":"Large Scale Private Learning via Low-rank Reparametrization","created":"2021-06-17","cited_by_count":88,"area":null},{"id":"2504.05295","title":"Dion: Distributed Orthonormalized Updates","created":"2025-04-07","cited_by_count":68,"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":"2103.03936","title":"Pufferfish: Communication-efficient Models At No Extra Cost","created":"2021-03-05","cited_by_count":61,"area":null},{"id":"2503.20762","title":"ASGO: Adaptive Structured Gradient Optimization","created":"2025-03-26","cited_by_count":57,"area":null},{"id":"2105.08339","title":"DRIVE: One-bit Distributed Mean Estimation","created":"2021-05-18","cited_by_count":52,"area":null},{"id":"2103.14272","title":"Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design","created":"2021-03-26","cited_by_count":47,"area":null},{"id":"2208.02507","title":"ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity","created":"2022-08-04","cited_by_count":45,"area":null},{"id":"2108.00951","title":"Rethinking gradient sparsification as total error minimization","created":"2021-08-02","cited_by_count":41,"area":null},{"id":"2303.10837","title":"FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System","created":"2023-03-20","cited_by_count":38,"area":null},{"id":"2202.08036","title":"No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices","created":"2022-02-16","cited_by_count":37,"area":null},{"id":"2110.10302","title":"Layer-wise Adaptive Model Aggregation for Scalable Federated Learning","created":"2021-10-19","cited_by_count":36,"area":null},{"id":"2206.09888","title":"SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression","created":"2022-06-20","cited_by_count":34,"area":null},{"id":"2202.07178","title":"Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy","created":"2022-02-15","cited_by_count":33,"area":null},{"id":"2209.15328","title":"Sparse Random Networks for Communication-Efficient Federated Learning","created":"2022-09-30","cited_by_count":32,"area":null},{"id":"2206.01299","title":"Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees","created":"2022-06-02","cited_by_count":30,"area":null},{"id":"2102.01593","title":"FEDZIP: A Compression Framework for Communication-Efficient Federated Learning","created":"2021-02-02","cited_by_count":27,"area":null},{"id":"2308.04604","title":"A Survey on Decentralized Federated Learning","created":"2023-08-08","cited_by_count":27,"area":null},{"id":"2103.00543","title":"On the Utility of Gradient Compression in Distributed Training Systems","created":"2021-02-28","cited_by_count":26,"area":null},{"id":"2208.12807","title":"Towards Federated Learning against Noisy Labels via Local Self-Regularization","created":"2022-08-25","cited_by_count":26,"area":null},{"id":"2102.08329","title":"An Information-Theoretic Justification for Model Pruning","created":"2021-02-16","cited_by_count":25,"area":null},{"id":"2301.00767","title":"A Survey on Federated Recommendation Systems","created":"2022-12-27","cited_by_count":25,"area":null},{"id":"2111.00092","title":"Optimal Compression of Locally Differentially Private Mechanisms","created":"2021-10-29","cited_by_count":22,"area":null},{"id":"2305.18469","title":"Reducing Communication for Split Learning by Randomized Top-k Sparsification","created":"2023-05-29","cited_by_count":22,"area":null},{"id":"2102.07245","title":"Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization","created":"2021-02-14","cited_by_count":21,"area":null},{"id":"2204.13169","title":"FedShuffle: Recipes for Better Use of Local Work in Federated Learning","created":"2022-04-27","cited_by_count":21,"area":null},{"id":"2207.03957","title":"Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox","created":"2022-07-08","cited_by_count":19,"area":null},{"id":"2105.11028","title":"Fast Federated Learning by Balancing Communication Trade-Offs","created":"2021-05-23","cited_by_count":18,"area":null},{"id":"2205.13054","title":"Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking","created":"2022-05-25","cited_by_count":18,"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":"2311.13171","title":"ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization","created":"2023-11-22","cited_by_count":18,"area":null},{"id":"2406.17660","title":"Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients","created":"2024-06-25","cited_by_count":18,"area":null},{"id":"2104.05245","title":"Distributed Learning Systems with First-order Methods","created":"2021-04-12","cited_by_count":17,"area":null},{"id":"2109.03194","title":"On the Convergence of Decentralized Adaptive Gradient Methods","created":"2021-09-07","cited_by_count":17,"area":null},{"id":"2111.05897","title":"Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters","created":"2021-11-10","cited_by_count":17,"area":null},{"id":"2206.10189","title":"A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates","created":"2022-06-21","cited_by_count":17,"area":null},{"id":"2105.07806","title":"Towards Demystifying Serverless Machine Learning Training","created":"2021-05-17","cited_by_count":16,"area":null},{"id":"2202.00280","title":"Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?","created":"2022-02-01","cited_by_count":16,"area":null},{"id":"2201.11865","title":"FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients","created":"2022-01-28","cited_by_count":15,"area":null},{"id":"2202.06009","title":"Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam","created":"2022-02-12","cited_by_count":15,"area":null},{"id":"2201.02664","title":"Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory","created":"2022-01-07","cited_by_count":14,"area":null},{"id":"2203.04925","title":"Correlated quantization for distributed mean estimation and optimization","created":"2022-03-09","cited_by_count":14,"area":null},{"id":"2304.14271","title":"A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services","created":"2023-04-13","cited_by_count":14,"area":null},{"id":"2410.15526","title":"SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training","created":"2024-10-20","cited_by_count":14,"area":null},{"id":"2106.02797","title":"Neural Distributed Source Coding","created":"2021-06-05","cited_by_count":13,"area":null},{"id":"2107.01499","title":"BAGUA: Scaling up Distributed Learning with System Relaxations","created":"2021-07-03","cited_by_count":13,"area":null},{"id":"2302.02390","title":"Quantized Distributed Training of Large Models with Convergence Guarantees","created":"2023-02-05","cited_by_count":13,"area":null},{"id":"2301.02654","title":"Does compressing activations help model parallel training?","created":"2023-01-06","cited_by_count":12,"area":null},{"id":"2506.01260","title":"Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism","created":"2025-06-02","cited_by_count":12,"area":null},{"id":"2507.08784","title":"Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees","created":"2025-07-11","cited_by_count":11,"area":null},{"id":"2105.04153","title":"Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates","created":"2021-05-10","cited_by_count":10,"area":null},{"id":"2404.06114","title":"Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey","created":"2024-04-09","cited_by_count":10,"area":null},{"id":"2102.08374","title":"IntSGD: Adaptive Floatless Compression of Stochastic Gradients","created":"2021-02-16","cited_by_count":9,"area":null},{"id":"2109.06710","title":"Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling","created":"2021-09-14","cited_by_count":9,"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":"2306.08881","title":"Evaluation and Optimization of Gradient Compression for Distributed Deep Learning","created":"2023-06-15","cited_by_count":7,"area":null},{"id":"2401.07788","title":"Activations and Gradients Compression for Model-Parallel Training","created":"2024-01-15","cited_by_count":7,"area":null},{"id":"2407.20830","title":"Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing","created":"2024-07-30","cited_by_count":7,"area":null},{"id":"2510.17802","title":"Unbiased Gradient Low-Rank Projection","created":"2025-10-20","cited_by_count":7,"area":null},{"id":"2103.00342","title":"Constrained Differentially Private Federated Learning for Low-bandwidth Devices","created":"2021-02-27","cited_by_count":6,"area":null},{"id":"2106.00564","title":"Wireless Federated Learning with Limited Communication and Differential Privacy","created":"2021-06-01","cited_by_count":6,"area":null},{"id":"2206.10452","title":"Shifted Compression Framework: Generalizations and Improvements","created":"2022-06-21","cited_by_count":6,"area":null},{"id":"2209.05148","title":"Personalized Federated Learning with Communication Compression","created":"2022-09-12","cited_by_count":6,"area":null},{"id":"2303.10434","title":"Byzantine-Resilient Federated Learning at Edge","created":"2023-03-18","cited_by_count":6,"area":null},{"id":"2306.12625","title":"Adaptive Compression in Federated Learning via Side Information","created":"2023-06-22","cited_by_count":6,"area":null},{"id":"2310.13033","title":"LASER: Linear Compression in Wireless Distributed Optimization","created":"2023-10-19","cited_by_count":6,"area":null},{"id":"2407.01378","title":"Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression","created":"2024-07-01","cited_by_count":6,"area":null},{"id":"2407.04480","title":"LoCo: Low-Bit Communication Adaptor for Large-scale Model Training","created":"2024-07-05","cited_by_count":6,"area":null},{"id":"2103.13026","title":"The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication","created":"2021-03-24","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":"2104.06023","title":"Communication Efficient Federated Learning with Adaptive Quantization","created":"2021-04-13","cited_by_count":4,"area":null},{"id":"2111.08617","title":"CGX: Adaptive System Support for Communication-Efficient Deep Learning","created":"2021-11-16","cited_by_count":4,"area":null},{"id":"2206.11239","title":"FedorAS: Federated Architecture Search under system heterogeneity","created":"2022-06-22","cited_by_count":4,"area":null},{"id":"2207.12779","title":"Reconciling Security and Communication Efficiency in Federated Learning","created":"2022-07-26","cited_by_count":4,"area":null},{"id":"2410.16029","title":"Natural GaLore: Accelerating GaLore for memory-efficient LLM Training and Fine-tuning","created":"2024-10-21","cited_by_count":4,"area":null},{"id":"2602.08923","title":"DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce","created":"2026-02-09","cited_by_count":4,"area":null},{"id":"2103.09424","title":"Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience","created":"2021-03-17","cited_by_count":3,"area":null},{"id":"2109.05222","title":"Fundamental limits of over-the-air optimization: Are analog schemes optimal?","created":"2021-09-11","cited_by_count":3,"area":null},{"id":"2111.08277","title":"Wyner-Ziv Gradient Compression for Federated Learning","created":"2021-11-16","cited_by_count":3,"area":null},{"id":"2202.02812","title":"Lossy Gradient Compression: How Much Accuracy Can One Bit Buy?","created":"2022-02-06","cited_by_count":3,"area":null},{"id":"2205.09470","title":"Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters","created":"2022-05-19","cited_by_count":3,"area":null},{"id":"2401.05518","title":"Correlated Quantization for Faster Nonconvex Distributed Optimization","created":"2024-01-10","cited_by_count":3,"area":null},{"id":"2405.10618","title":"Distributed Event-Based Learning via ADMM","created":"2024-05-17","cited_by_count":3,"area":null},{"id":"2507.15816","title":"Federated Split Learning with Improved Communication and Storage Efficiency","created":"2025-07-21","cited_by_count":3,"area":null},{"id":"2601.21929","title":"LoRIF: Low-Rank Influence Functions for Scalable Training Data Attribution","created":"2026-01-29","cited_by_count":3,"area":null},{"id":"2108.07827","title":"Compressing gradients by exploiting temporal correlation in momentum-SGD","created":"2021-08-17","cited_by_count":2,"area":null},{"id":"2111.10672","title":"Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning Clusters","created":"2021-11-20","cited_by_count":2,"area":null},{"id":"2302.09634","title":"Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity","created":"2023-02-19","cited_by_count":2,"area":null},{"id":"2310.15976","title":"Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization","created":"2023-10-24","cited_by_count":2,"area":null},{"id":"2310.19558","title":"Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification","created":"2023-10-30","cited_by_count":2,"area":null},{"id":"2312.08053","title":"Kimad: Adaptive Gradient Compression with Bandwidth Awareness","created":"2023-12-13","cited_by_count":2,"area":null},{"id":"2312.13795","title":"Sparse Training for Federated Learning with Regularized Error Correction","created":"2023-12-21","cited_by_count":2,"area":null},{"id":"2402.14925","title":"Efficient Unbiased Sparsification","created":"2024-02-22","cited_by_count":2,"area":null},{"id":"2403.02347","title":"On the Convergence of Federated Learning Algorithms without Data Similarity","created":"2024-02-29","cited_by_count":2,"area":null},{"id":"2409.04022","title":"Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression","created":"2024-09-06","cited_by_count":2,"area":null},{"id":"2409.14893","title":"Novel Gradient Sparsification Algorithm via Bayesian Inference","created":"2024-09-23","cited_by_count":2,"area":null}],"next_cursor":"100"}