{"total":23,"rows":[{"id":"2405.17991","title":"VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections","created":"2024-05-28","cited_by_count":18,"area":null},{"id":"2401.16265","title":"CO2: Efficient Distributed Training with Full Communication-Computation Overlap","created":"2024-01-29","cited_by_count":17,"area":null},{"id":"2301.09830","title":"Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression","created":"2023-01-24","cited_by_count":15,"area":null},{"id":"2508.15706","title":"Overcoming the Communication-Performance Tradeoff in LLM Pretraining","created":"2025-08-21","cited_by_count":12,"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":"2209.01346","title":"HammingMesh: A Network Topology for Large-Scale Deep Learning","created":"2022-09-03","cited_by_count":8,"area":null},{"id":"2302.07615","title":"Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities","created":"2023-02-15","cited_by_count":7,"area":null},{"id":"2306.08289","title":"$\\textbf{A}^2\\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning","created":"2023-06-14","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":"2402.07529","title":"Accelerating Distributed Deep Learning using Lossless Homomorphic Compression","created":"2024-02-12","cited_by_count":4,"area":null},{"id":"2302.07475","title":"Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning","created":"2023-02-15","cited_by_count":3,"area":null},{"id":"2403.16372","title":"SignSGD with Federated Voting","created":"2024-03-25","cited_by_count":2,"area":null},{"id":"2604.24088","title":"TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training","created":"2026-04-27","cited_by_count":2,"area":null},{"id":"2304.07613","title":"STen: Productive and Efficient Sparsity in PyTorch","created":"2023-04-15","cited_by_count":1,"area":null},{"id":"2402.01340","title":"SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding","created":"2024-02-02","cited_by_count":1,"area":null},{"id":"2505.23523","title":"Efficient AllReduce with Stragglers","created":"2025-05-29","cited_by_count":1,"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":"2305.18627","title":"Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees","created":"2023-05-29","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":"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":"2601.19132","title":"In-Network Collective Operations: Game Changer or Challenge for AI Workloads?","created":"2026-01-27","cited_by_count":0,"area":null},{"id":"2607.01678","title":"SCAPE: Accurate and Efficient LLM Training with Extreme Sparse Communication","created":"2026-07-02","cited_by_count":0,"area":null},{"id":"2608.12573","title":"Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection","created":"2026-08-12","cited_by_count":0,"area":null}],"next_cursor":null}