Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints

by   Mónica Ribero, et al.

Federated learning systems facilitate training of global models in settings where potentially heterogeneous data is distributed across a large number of clients. Such systems operate in settings with intermittent client availability and/or time-varying communication constraints. As a result, the global models trained by federated learning systems may be biased towards clients with higher availability. We propose F3AST, an unbiased algorithm that dynamically learns an availability-dependent client selection strategy which asymptotically minimizes the impact of client-sampling variance on the global model convergence, enhancing performance of federated learning. The proposed algorithm is tested in a variety of settings for intermittently available clients under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets. We show up to 186 improvements over FedAvg, and 8 Shakespeare, respectively.


Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning

Due to communication constraints and intermittent client availability in...

Active Federated Learning

Federated Learning allows for population level models to be trained with...

Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies

Federated learning is a distributed optimization paradigm that enables a...

Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability

Federated learning is a new distributed machine learning framework, wher...

Detailed comparison of communication efficiency of split learning and federated learning

We compare communication efficiencies of two compelling distributed mach...

Subspace Learning for Personalized Federated Optimization

As data is generated and stored almost everywhere, learning a model from...

Is Non-IID Data a Threat in Federated Online Learning to Rank?

In this perspective paper we study the effect of non independent and ide...