{"total":55,"rows":[{"id":"2307.10616","title":"Heterogeneous Federated Learning: State-of-the-art and Research Challenges","created":"2023-07-20","cited_by_count":161,"area":null},{"id":"2504.19874","title":"TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate","created":"2025-04-28","cited_by_count":125,"area":null},{"id":"2505.14669","title":"Quartet: Native FP4 Training Can Be Optimal for Large Language Models","created":"2025-05-20","cited_by_count":39,"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":"2302.00543","title":"DoCoFL: Downlink Compression for Cross-Device Federated Learning","created":"2023-02-01","cited_by_count":28,"area":null},{"id":"2310.11028","title":"Matrix Compression via Randomized Low Rank and Low Precision Factorization","created":"2023-10-17","cited_by_count":25,"area":null},{"id":"2502.05003","title":"QuEST: Stable Training of LLMs with 1-Bit Weights and Activations","created":"2025-02-07","cited_by_count":22,"area":null},{"id":"2411.17525","title":"Pushing the Limits of Large Language Model Quantization via the Linearity Theorem","created":"2024-11-26","cited_by_count":21,"area":null},{"id":"2601.22813","title":"Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation","created":"2026-01-30","cited_by_count":21,"area":null},{"id":"2302.08545","title":"THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression","created":"2023-02-16","cited_by_count":15,"area":null},{"id":"2306.04444","title":"Fast Optimal Locally Private Mean Estimation via Random Projections","created":"2023-06-07","cited_by_count":14,"area":null},{"id":"2306.04924","title":"Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation","created":"2023-06-08","cited_by_count":9,"area":null},{"id":"2210.07376","title":"ScionFL: Efficient and Robust Secure Quantized Aggregation","created":"2022-10-13","cited_by_count":8,"area":null},{"id":"2604.18555","title":"A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work","created":"2026-04-20","cited_by_count":8,"area":null},{"id":"2605.13810","title":"Provable Quantization with Randomized Hadamard Transform","created":"2026-05-13","cited_by_count":8,"area":"inference"},{"id":"2110.02065","title":"SDR: Efficient Neural Re-ranking using Succinct Document Representation","created":"2021-10-03","cited_by_count":7,"area":null},{"id":"2205.13341","title":"QUIC-FL: Quick Unbiased Compression for Federated Learning","created":"2022-05-26","cited_by_count":7,"area":null},{"id":"2212.01523","title":"GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning","created":"2022-12-03","cited_by_count":7,"area":null},{"id":"2601.17187","title":"High-Rate Quantized Matrix Multiplication I","created":"2026-01-23","cited_by_count":7,"area":null},{"id":"2303.08423","title":"Communication-Efficient Design for Quantized Decentralized Federated Learning","created":"2023-03-15","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":"2604.07796","title":"Order-Optimal Sequential 1-Bit Mean Estimation in General Tail Regimes","created":"2026-04-09","cited_by_count":6,"area":null},{"id":"2605.06014","title":"Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven","created":"2026-05-07","cited_by_count":6,"area":"inference"},{"id":"2403.00873","title":"Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions","created":"2024-03-01","cited_by_count":5,"area":null},{"id":"2509.21940","title":"Sequential 1-bit Mean Estimation with Near-Optimal Sample Complexity","created":"2025-09-26","cited_by_count":5,"area":null},{"id":"2408.03220","title":"Masked Random Noise for Communication Efficient Federated Learning","created":"2024-08-06","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":"2310.18868","title":"Correlation Aware Sparsified Mean Estimation Using Random Projection","created":"2023-10-29","cited_by_count":3,"area":null},{"id":"2311.09270","title":"FedCode: Communication-Efficient Federated Learning via Transferring Codebooks","created":"2023-11-15","cited_by_count":3,"area":null},{"id":"2406.09152","title":"EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters","created":"2024-06-13","cited_by_count":3,"area":null},{"id":"2604.19528","title":"Revisiting RaBitQ and TurboQuant: A Symmetric Comparison of Methods, Theory, and Experiments","created":"2026-04-21","cited_by_count":3,"area":null},{"id":"2311.17299","title":"Federated Fine-Tuning of Foundation Models via Probabilistic Masking","created":"2023-11-29","cited_by_count":2,"area":null},{"id":"2402.03158","title":"Optimal and Near-Optimal Adaptive Vector Quantization","created":"2024-02-05","cited_by_count":2,"area":null},{"id":"2504.15366","title":"FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching","created":"2025-04-21","cited_by_count":2,"area":null},{"id":"2604.23418","title":"Approximating Uniform Random Rotations by Two-Stage Structured Hadamard Rotations in High Dimensions","created":"2026-04-25","cited_by_count":2,"area":null},{"id":"2607.18745","title":"Contraction-Gauge Preconditioning for Quantized Matrix Multiplication","created":"2026-07-21","cited_by_count":2,"area":null},{"id":"2409.13451","title":"Noise-Robust and Resource-Efficient ADMM-based Federated Learning","created":"2024-09-20","cited_by_count":1,"area":null},{"id":"2503.11897","title":"PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors","created":"2025-03-14","cited_by_count":1,"area":null},{"id":"2511.13144","title":"Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching","created":"2025-11-17","cited_by_count":1,"area":null},{"id":"2511.19705","title":"CafeQ: Calibration-free Quantization via Learned Transformations and Adaptive Rounding","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2608.18147","title":"Entropy-Constrained Adaptive Stochastic Quantization","created":"2026-08-10","cited_by_count":1,"area":null},{"id":"2609.24298","title":"KV-COBRA: KV Cache Compression via Co-Optimized Bit-Rank Allocation","created":"2026-09-21","cited_by_count":1,"area":"inference"},{"id":"2605.19972","title":"Block-Sphere Vector Quantization","created":"2026-05-19","cited_by_count":0,"area":null},{"id":"2608.05127","title":"SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant","created":"2026-08-05","cited_by_count":0,"area":null},{"id":"2608.11240","title":"VQ-bench: A Composable Vector Quantization Framework","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2608.30384","title":"RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Search","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.04830","title":"Communication-Efficient Personalized Federated Learning via Layer-Wise Multi-Threshold Random Sketching","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2609.08759","title":"A Note on Scaling in Randomly Rotated Quantization and Its Connection to the CDEF +1 Pythagorean Relation","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2609.36032","title":"TORQUE: Optimizing What (not) to Quantize Before and After Rotation","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36610","title":"Communication-Efficient Agnostic Federated Learning via Faster Convergence and Compression","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.09202","title":"Few Bits, One Law: Toward W2A4KV2","created":"2026-10-06","cited_by_count":0,"area":"inference"},{"id":"2610.12514","title":"CORAL-KV: Compact Cross-Layer Prediction and Residual Coding for Autoregressive Video KV Caches","created":"2026-10-07","cited_by_count":0,"area":"diffusion"},{"id":"2610.16289","title":"Fast Quantized Johnson$-$Lindenstrauss Transforms","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}