Towards Effective Device-Aware Federated Learning

TL;DR

This paper advances federated learning by incorporating multi-criteria, device-aware contribution weighting and an adaptive aggregation mechanism, improving model performance while preserving privacy.

Contribution

It introduces a multi-criteria, device-specific contribution computation and an online-adjustable aggregation operator for federated learning.

Findings

Enhanced model accuracy over FedAvg baseline

Effective device contribution weighting improves learning

Adaptive aggregation parameters optimize performance

Abstract

With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security. To address the above issues, Federated Learning (FL) has been recently proposed as a means to leave data and computational resources distributed over a large number of nodes (clients) where a central coordinating server aggregates only locally computed updates without knowing the original data. In this work, we extend the FL framework by pushing forward the state the art in the field on several dimensions: (i) unlike the original FedAvg approach relying solely on single criteria (i.e., local dataset size), a suite of domain- and client-specific criteria constitute the basis to compute each local client's contribution, (ii) the multi-criteria contribution of each device is computed in a prioritized fashion by leveraging a priority-aware aggregation operator used in the field of information retrieval, and (iii) a mechanism is proposed for online-adjustment of the aggregation operator parameters via a local search strategy with backtracking. Extensive experiments on a publicly available dataset indicate the merits of the proposed approach compared to standard FedAvg baseline.