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A Multi-Token Coordinate Descent Method for Vertical Federated Learning
Pedro Valdeira · Yuejie Chi · Claudia Soares · Joao Xavier

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in Federated Learning: Recent Advances and New Challenges »
Event URL: https://openreview.net/forum?id=z5ZmWVh0HCh »

Communication efficiency is a major challenge in federated learning. In client-server schemes, the server constitutes a bottleneck, and while decentralized setups spread communications, they do not reduce them. We propose a communication efficient semi-decentralized federated learning algorithm for feature-distributed data. Our multi-token method can be seen as a parallel Markov chain (block) coordinate descent algorithm. In this work, we formalize the multi-token semi-decentralized scheme, which subsumes the client-server and decentralized setups, and design a feature-distributed learning algorithm for this setup. Numerical results show the improved communication efficiency of our algorithm.

Author Information

Pedro Valdeira (Carnegie Mellon University)
Yuejie Chi (Carnegie Mellon University)
Claudia Soares (NOVA School of Science and Technology)
Joao Xavier (Instituto Superior Tecnico)

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