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Distributed Learning

1100 directly classified papers

Papers per year

Papers

Delegating Data Collection in Decentralized Machine Learning AISTATS 2024
SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization NIPS 2024
Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization AISTATS 2024
Sketching for Distributed Deep Learning: A Sharper Analysis NIPS 2024
DAGER: Exact Gradient Inversion for Large Language Models NIPS 2024
Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations NIPS 2024
Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization JMLR 2024
SpeedLoader: An I/O efficient scheme for heterogeneous and distributed LLM operation NIPS 2024
Communication-Efficient Federated Learning With Data and Client Heterogeneity AISTATS 2024
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates AISTATS 2024
Rethinking Memory and Communication Costs for Efficient Data Parallel Training of Large Language Models NIPS 2024
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models ACL 2024
Recovery Guarantees for Distributed-OMP AISTATS 2024
AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms AISTATS 2024
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning NIPS 2024
Compression with Exact Error Distribution for Federated Learning AISTATS 2024
pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning NIPS 2024
Parallel Empirical Evaluations: Resilience despite Concurrency AAAI 2024
Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization AAAI 2024
Pipeline Parallelism with Controllable Memory NIPS 2024
Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis JMLR 2024
SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization NIPS 2024
An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract) AAAI 2024
On the Necessity of Collaboration for Online Model Selection with Decentralized Data NIPS 2024
Traceable Federated Continual Learning CVPR 2024