Communication Efficient Fair Federated Recommender System

KIRANDEEP KAUR, Sujit Gujar, Shweta Jain

Keywords: Federated Learning, Recommender Systems, Bias and Fairness

TL;DR: A random sampling based Fair Federated Recommender System.

Abstract: Federated Recommender Systems (FRSs) aim to provide recommendations to clients in a distributed manner with privacy preservation. FRSs suffer from high communication costs due to the communication between the server and many clients. Some past literature on federated supervised learning shows that sampling clients randomly improve communication efficiency without jeopardizing accuracy. However, each user is considered a separate client in FRS and clients communicate only item gradients. Thus, incorporating random sampling and determining the number of clients to be sampled in each communication round to retain the model's accuracy in FRS becomes challenging. This paper provides sample complexity bounds on the number of clients that must be sampled in an FRS to preserve accuracy. Next, we consider the issue of demographic bias in FRS, quantified as the difference in the average error rates across different groups. Supervised learning algorithms mitigate the group bias by adding the fairness constraint in the training loss, which requires sharing protected attributes with the server. This is prohibited in a federated setting to ensure clients' privacy. We design \ouralgo, a Random Sampling based Fair Federated Recommender System, which trains to achieve a fair global model. In addition, it also trains local clients towards a fair global model to reduce demographic bias at the client level without the need to share their protected attributes. We empirically demonstrate all our results across the two most popular real-world datasets (ML1M, ML100k) and different sensitive features (age and gender) to prove that RS-FairFRS helps reduce communication cost and demographic bias with improved model accuracy.

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