Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine
learning models without exchanging their private data. Although FL emerged as a privacy-
preserving alternative to centralized machine learning approaches, it faces significant
challenges in preserving the privacy of its clients and mitigating potential bias against clients
or disadvantaged groups. Most existing research in FL has addressed these two ethical …

Federated learning (FL) as distributed machine learning has gained popularity as privacy-
aware Machine Learning (ML) systems have emerged as a technique that prevents privacy
leakage by building a global model and by conducting individualized training of
decentralized edge clients on their own private data. The existing works, however, employ
privacy mechanisms such as Secure Multiparty Computing (SMC), Differential Privacy (DP),
etc. Which are immensely susceptible to interference, massive computational overhead, low …