Fairness and Privacy-Preserving in Federated Learning: A Survey. (arXiv:2306.08402v2 [cs.CR] UPDATED)

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 accuracy, etc.
With the increasingly broad deployment of FL …

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