핵심 개념
LoCoDL combines Local Training and Communication Compression to achieve highly communication-efficient distributed learning.
초록
Introduction
Federated Learning (FL) leverages distributed training.
Challenges include data privacy and communication efficiency.
Proposed Algorithm LoCoDL
Utilizes Local Training and Communication Compression.
Converges to a consensus solution.
Convergence and Complexity of LoCoDL
Linear convergence with doubly-accelerated communication complexity.
Experiments
Outperforms existing algorithms in communication efficiency.
Comparison with other methods on logistic regression datasets.
Conclusion
LoCoDL sets new standards in communication efficiency for distributed learning.
통계
LoCoDL는 Local Training 및 Communication Compression을 결합하여 높은 통신 효율을 달성합니다.