EthicalFL - An Optimized Federated Learning Framework with Bias Mitigation

Abstract

Federated learning helps one leverage AI/ML techniques while preserving the privacy of localized data. However, owing to its decentralized nature, Federated Learning faces several optimization issues. This paper identifies the problem of incoming network congestion concerning the Aggregator in a federated scenario and proposes a statistical significance test to address the problem. Further network optimization is done by implementing a requirement-based request-response communication architecture to reduce unnecessary training rounds. This research also targets the infamous bias problem introduced due to label bias at the clients in a cross-device FL setting. The proposed solution defines a novel biasing factor to tackle data bias in the Aggregated model per the principles of AI ethics without violating privacy norms.