Federated Learning (FL) [1]provides acommunication-efficient andprivacypreserving means to learn from data distributed across clients such as cell phones, autonomous vehicles, and hospitals. FL aims for each client to benefit from collaborating in the learning process without sacrificing data privacy or paying a substantial communication cost. Federated Averaging (FedAvg) [1] is the predominant FL algorithm.

Throughout this paper, all convexity-related concepts are defined in the Euclidean space.

Despite its pilot progress, personalized federated learning with differential privacy still faces numerous challenges.

Federated Learning (FL) is an emerging collaborative machine learning framework where multiple clients train the global model without sharing their own datasets.

This has motivated numerous studies aiming to reduce the variance and improve convergence of FL on non-IID data [6, 9, 14, 17, 19, 30]. On another note, constraints on communication resources and therefore on the number of clients that may participate in training additionally complicate implementation of FL schemes.

In this work, we propose a peer-to-peer (P2P) learning scheme that is secure against malicious servers and robust to malicious clients.

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