Distributed Fixed Point Methods with Compressed Iterates
This work addresses communication efficiency in distributed optimization for federated learning, but it is incremental as it builds on existing compression techniques.
The authors tackled the problem of analyzing iterative optimization methods with compressed iterates under basic assumptions, motivated by federated learning, and developed standard and variance reduced methods with established communication complexity bounds.
We propose basic and natural assumptions under which iterative optimization methods with compressed iterates can be analyzed. This problem is motivated by the practice of federated learning, where a large model stored in the cloud is compressed before it is sent to a mobile device, which then proceeds with training based on local data. We develop standard and variance reduced methods, and establish communication complexity bounds. Our algorithms are the first distributed methods with compressed iterates, and the first fixed point methods with compressed iterates.