  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 systems, it is challenging to
ensure fairness and maintain active client participation in FL systems. Very
few works ensure reasonably satisfactory performances for the numerous diverse
clients and fail to prevent potential bias against particular demographics in
FL systems. The current efforts fail to strike a compromise between privacy,
fairness, and model performance in FL systems and are vulnerable to a number of
additional problems. In this paper, we provide a comprehensive survey stating
the basic concepts of FL, the existing privacy challenges, techniques, and
relevant works concerning privacy in FL. We also provide an extensive overview
of the increasing fairness challenges, existing fairness notions, and the
limited works that attempt both privacy and fairness in FL. By comprehensively
describing the existing FL systems, we present the potential future directions
pertaining to the challenges of privacy-preserving and fairness-aware FL
systems.
