{"total":20,"rows":[{"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":"2404.05196","title":"HSViT: Horizontally Scalable Vision Transformer","created":"2024-04-08","cited_by_count":6,"area":null},{"id":"2204.03326","title":"Enabling All In-Edge Deep Learning: A Literature Review","created":"2022-04-07","cited_by_count":4,"area":null},{"id":"2405.13746","title":"CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models","created":"2024-05-22","cited_by_count":4,"area":null},{"id":"2511.22616","title":"Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers","created":"2025-11-27","cited_by_count":3,"area":null},{"id":"2110.07649","title":"Federated learning and next generation wireless communications: A survey on bidirectional relationship","created":"2021-10-14","cited_by_count":2,"area":null},{"id":"2302.11180","title":"DISCO: Distributed Inference with Sparse Communications","created":"2023-02-22","cited_by_count":2,"area":null},{"id":"2508.07706","title":"Energy Consumption in Parallel Neural Network Training","created":"2025-08-11","cited_by_count":1,"area":null},{"id":"2601.10491","title":"Communication-Efficient Federated Learning by Exploiting Spatio-Temporal Correlations of Gradients","created":"2026-01-15","cited_by_count":1,"area":null},{"id":"2205.13216","title":"Encoded Gradients Aggregation against Gradient Leakage in Federated Learning","created":"2022-05-26","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":"2310.00967","title":"MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training","created":"2023-10-02","cited_by_count":0,"area":null},{"id":"2311.03061","title":"Learned layered coding for Successive Refinement in the Wyner-Ziv Problem","created":"2023-11-06","cited_by_count":0,"area":null},{"id":"2312.17530","title":"RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation","created":"2023-12-29","cited_by_count":0,"area":null},{"id":"2402.13781","title":"Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning","created":"2024-02-21","cited_by_count":0,"area":null},{"id":"2405.01067","title":"AB-Training: A Communication-Efficient Approach for Distributed Low-Rank Learning","created":"2024-05-02","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":"2603.12976","title":"SCOPE: Semantic Coreset with Orthogonal Projection Embeddings for Federated learning","created":"2026-03-13","cited_by_count":0,"area":null},{"id":"2603.18112","title":"Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training","created":"2026-03-18","cited_by_count":0,"area":null},{"id":"2604.14751","title":"Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations","created":"2026-04-16","cited_by_count":0,"area":null}],"next_cursor":null}