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Distributed Learning

123 directly classified papers

Papers per year

Papers

EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity AAAI 2025
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness AAAI 2025
Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning AAAI 2025
DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework AAAI 2025
Scalable Decentralized Algorithms for Online Personalized Mean Estimation AAAI 2025
ZEN: Empowering Distributed Training with Sparsity-driven Data Synchronization OSDI 2025
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling AAAI 2025
Capture Global Feature Statistics for One-Shot Federated Learning AAAI 2025
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors CVPR 2025
Stability and Generalization of Zeroth-Order Decentralized Stochastic Gradient Descent with Changing Topology AAAI 2025
Distributed Cascaded Manifold Hashing Network for Compact Image Set Representation IJCAI 2025
JointSQ: Joint Sparsification-Quantization for Distributed Learning CVPR 2024
Thinking Forward: Memory-Efficient Federated Finetuning of Language Models NIPS 2024
A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning CVPR 2024
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models NIPS 2024
Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD NIPS 2024
An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract) AAAI 2024
Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML NIPS 2024
SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization NIPS 2024
Communication Efficient Distributed Newton Method over Unreliable Networks AAAI 2024
Distributed Manifold Hashing for Image Set Classification and Retrieval AAAI 2024
Incorporating Serverless Computing into P2P Networks for ML Training: In-Database Tasks and Their Scalability Implications (Student Abstract) AAAI 2024
Byzantine-Robust Decentralized Learning via Remove-then-Clip Aggregation AAAI 2024
Exponential Quantum Communication Advantage in Distributed Inference and Learning NIPS 2024
Federated Learning over Connected Modes NIPS 2024