{"total":17,"rows":[{"id":"2108.06098","title":"FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning","created":"2021-08-13","cited_by_count":141,"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":"2102.01386","title":"AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning","created":"2021-02-02","cited_by_count":48,"area":null},{"id":"2108.00951","title":"Rethinking gradient sparsification as total error minimization","created":"2021-08-02","cited_by_count":41,"area":null},{"id":"2201.06227","title":"Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing","created":"2022-01-17","cited_by_count":22,"area":null},{"id":"2211.00188","title":"Adaptive Compression for Communication-Efficient Distributed Training","created":"2022-10-31","cited_by_count":16,"area":null},{"id":"2305.12201","title":"GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training","created":"2023-05-20","cited_by_count":10,"area":null},{"id":"2109.05613","title":"Critical Learning Periods in Federated Learning","created":"2021-09-12","cited_by_count":8,"area":null},{"id":"2301.08897","title":"ScaDLES: Scalable Deep Learning over Streaming data at the Edge","created":"2023-01-21","cited_by_count":8,"area":null},{"id":"2307.07950","title":"Accelerating Distributed ML Training via Selective Synchronization","created":"2023-07-16","cited_by_count":7,"area":null},{"id":"2312.02493","title":"Flexible Communication for Optimal Distributed Learning over Unpredictable Networks","created":"2023-12-05","cited_by_count":3,"area":null},{"id":"2312.08053","title":"Kimad: Adaptive Gradient Compression with Bandwidth Awareness","created":"2023-12-13","cited_by_count":2,"area":null},{"id":"2310.17972","title":"EcoLearn: Optimizing the Carbon Footprint of Federated Learning","created":"2023-10-27","cited_by_count":1,"area":null},{"id":"2110.01595","title":"Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients","created":"2021-10-04","cited_by_count":0,"area":null},{"id":"2307.03500","title":"DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification","created":"2023-07-07","cited_by_count":0,"area":null},{"id":"2509.10537","title":"On Using Large-Batches in Federated Learning","created":"2025-09-05","cited_by_count":0,"area":null},{"id":"2605.01989","title":"DBLP: Phase-Aware Bounded-Loss Transport for Burst-Resilient Distributed ML Training","created":"2026-05-03","cited_by_count":0,"area":null}],"next_cursor":null}