Distributed Learning

Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration JMLR 2025 Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity JMLR 2025 Decentralized Diffusion Models CVPR 2025 Decentralized Federated Learning with Model Caching on Mobile Agents AAAI 2025 Decentralized Low-Rank Fine-Tuning of Large Language Models ACL 2025 Decentralized Projected Riemannian Stochastic Recursive Momentum Method for Nonconvex Optimization AAAI 2025 Decentralized Sparse Linear Regression via Gradient-Tracking JMLR 2025 Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees ICLR 2025 Deep Distributed Optimization for Large-Scale Quadratic Programming ICLR 2025 dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis CVPR 2025 DICE: Data Influence Cascade in Decentralized Learning ICLR 2025 DICE: Staleness-Centric Optimizations for Parallel Diffusion MoE Inference ICCV 2025 Differential Privacy in Distributed Learning: Beyond Uniformly Bounded Stochastic Gradients AISTATS 2025 DISC: Backpressure Mitigation In Multi-tier Applications With Distributed Shared Connection NSDI 2025 Distributed Cascaded Manifold Hashing Network for Compact Image Set Representation IJCAI 2025 Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication EMNLP 2025 DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agent ICLR 2025 DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately AAAI 2025 EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models EMNLP 2025 EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback JMLR 2025 EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity AAAI 2025 Enabling Efficient GPU Communication over Multiple NICs with FuseLink OSDI 2025 Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning IJCAI 2025 Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning AAAI 2025 FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning IJCAI 2025