Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees
This work addresses efficiency and scalability issues for distributed or federated learning systems with heterogeneous devices, offering incremental improvements over existing methods.
The paper tackles communication constraints and device partial participation in distributed or federated learning by introducing Artemis, a framework that uses bidirectional compression and memory mechanisms, achieving linear convergence rates under weak assumptions and providing experimental validation.
We introduce a framework - Artemis - to tackle the problem of learning in a distributed or federated setting with communication constraints and device partial participation. Several workers (randomly sampled) perform the optimization process using a central server to aggregate their computations. To alleviate the communication cost, Artemis allows to compress the information sent in both directions (from the workers to the server and conversely) combined with a memory mechanism. It improves on existing algorithms that only consider unidirectional compression (to the server), or use very strong assumptions on the compression operator, and often do not take into account devices partial participation. We provide fast rates of convergence (linear up to a threshold) under weak assumptions on the stochastic gradients (noise's variance bounded only at optimal point) in non-i.i.d. setting, highlight the impact of memory for unidirectional and bidirectional compression, analyze Polyak-Ruppert averaging. We use convergence in distribution to obtain a lower bound of the asymptotic variance that highlights practical limits of compression. We propose two approaches to tackle the challenging case of devices partial participation and provide experimental results to demonstrate the validity of our analysis.