Log In Sign Up

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

by   Yae Jee Cho, et al.

Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Several works have analyzed the convergence of federated learning by accounting of data heterogeneity, communication and computation limitations, and partial client participation. However, they assume unbiased client participation, where clients are selected at random or in proportion of their data sizes. In this paper, we present the first convergence analysis of federated optimization for biased client selection strategies, and quantify how the selection bias affects convergence speed. We reveal that biasing client selection towards clients with higher local loss achieves faster error convergence. Using this insight, we propose Power-of-Choice, a communication- and computation-efficient client selection framework that can flexibly span the trade-off between convergence speed and solution bias. Our experiments demonstrate that Power-of-Choice strategies converge up to 3 × faster and give 10


page 1

page 2

page 3

page 4


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

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

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

Federated learning systems facilitate training of global models in setti...

Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning

Shapley Value is commonly adopted to measure and incentivize client part...

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

In federated optimization, heterogeneity in the clients' local datasets ...

HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask

Federated learning alleviates the privacy risk in distributed learning b...

Data Analysis: Communicating with Offshore Vendors using Instant Messaging Services

The purpose of this study is to find whether the choice of correct analy...

Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity

Federated edge learning (FEEL) has attracted much attention as a privacy...