Communication-Efficient Federated Learning via Optimal Client Sampling
- URL: http://arxiv.org/abs/2007.15197v2
- Date: Wed, 14 Oct 2020 19:08:30 GMT
- Title: Communication-Efficient Federated Learning via Optimal Client Sampling
- Authors: Monica Ribero, Haris Vikalo
- Abstract summary: Federated learning (FL) ameliorates privacy concerns in settings where a central server coordinates learning from data distributed across many clients.
We propose a novel, simple and efficient way of updating the central model in communication-constrained settings.
We test this policy on a synthetic dataset for logistic regression and two FL benchmarks, namely, a classification task on EMNIST and a realistic language modeling task.
- Score: 20.757477553095637
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated learning (FL) ameliorates privacy concerns in settings where a
central server coordinates learning from data distributed across many clients.
The clients train locally and communicate the models they learn to the server;
aggregation of local models requires frequent communication of large amounts of
information between the clients and the central server. We propose a novel,
simple and efficient way of updating the central model in
communication-constrained settings based on collecting models from clients with
informative updates and estimating local updates that were not communicated. In
particular, modeling the progression of model's weights by an
Ornstein-Uhlenbeck process allows us to derive an optimal sampling strategy for
selecting a subset of clients with significant weight updates. The central
server collects updated local models from only the selected clients and
combines them with estimated model updates of the clients that were not
selected for communication. We test this policy on a synthetic dataset for
logistic regression and two FL benchmarks, namely, a classification task on
EMNIST and a realistic language modeling task using the Shakespeare dataset.
The results demonstrate that the proposed framework provides significant
reduction in communication while maintaining competitive or achieving superior
performance compared to a baseline. Our method represents a new line of
strategies for communication-efficient FL that is orthogonal to the existing
user-local methods such as quantization or sparsification, thus complementing
rather than aiming to replace those existing methods.
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