T2F2L: Towards Trustworthy Fairness in Federated Learning Through A Verifiable Approach

Federated learning (FL) has emerged as a privacy-preserving alternative to traditional machine learning, enabling collaborative model training without centralizing users’ data. While this decentralized approach enhances privacy, it introduces new trust assumptions. Specifically, that users honestly participate in the protocol. Simultaneously, the growing emphasis on fairness in machine learning has led to the development of fairness-aware FL schemes, which aim to ensure equitable model performance across diverse user groups. However, existing solutions for fair FL schemes rely on users truthfully reporting fairness-related metrics. This trust assumption opens the door to malicious behavior: users may manipulate these statistics to influence the global model, degrading its fairness, and/or its overall accuracy. Current frameworks lack mechanisms to detect or prevent such adversarial manipulation. This paper addresses this critical gap through two main contributions. First, as an example to illustrate our point, we empirically demonstrate the vulnerability of a representative fairness-aware FL framework to targeted attacks that exploit unverified fairness computations. Second, and most importantly, we propose a novel scheme (that can be integrated into existing fair FL schemes) that augments fairness-aware FL with verifiability. Our solution enables the detection of dishonest participants without compromising user privacy, thus strengthening the robustness of fairness-aware FL schemes.