Communication Bounds for the Distributed Experts Problem
This work addresses communication efficiency in distributed learning for experts problems, which is incremental as it builds on existing distributed settings with new protocols.
The paper tackled the distributed experts problem by proposing the first communication-efficient protocols that achieve near-optimal regret across various communication models and aggregation functions, even against adaptive adversaries, and demonstrated empirical savings on HPO-B benchmarks.
In this work, we study the experts problem in the distributed setting where an expert's cost needs to be aggregated across multiple servers. Our study considers various communication models such as the message-passing model and the broadcast model, along with multiple aggregation functions, such as summing and taking the $\ell_p$ norm of an expert's cost across servers. We propose the first communication-efficient protocols that achieve near-optimal regret in these settings, even against a strong adversary who can choose the inputs adaptively. Additionally, we give a conditional lower bound showing that the communication of our protocols is nearly optimal. Finally, we implement our protocols and demonstrate empirical savings on the HPO-B benchmarks.