Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization

arXiv:2310.07983 · cs.LG, math.OC, stat.ML · Submitted 2023-10-12 · Read on arXiv

cs.LG, math.OC, stat.ML

Submitted: 2023-10-12

Updated: 2026-09-14

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 12× in this paper · explained in 1 episode
• Federated Learning — 9× in this paper · explained in 14 episodes
• Data Heterogeneity — 3× in this paper · explained in 3 episodes
• Lyapunov function — 2× in this paper · explained in 2 episodes
• Adaptivity — 1× in this paper · explained in 1 episode
• Decentralized Learning — 1× in this paper · explained in 2 episodes
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Variance Reduced Local SGD with Lower Communication Complexity
• TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation
• CompressedScaffnew: The First Theoretical Double Acceleration of Communication from Local Training and Compression in Distributed Optimization

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