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Federated Learning (FL) enables the training of Deep Learning models withoutcentrally collecting possibly sensitive raw data. This paves the way forstronger privacy guarantees when building predictive models. The most usedalgorithms for FL are parameter-averaging based schemes (e.g., FederatedAveraging) that, however, have well known limits: (i) Clients must implementthe same model architecture; (ii) Transmitting model weights and model updatesimplies high communication cost, which scales up with the number of modelparameters; (iii) In presence of non-IID data distributions,parameter-averaging aggregation schemes perform poorly due to client modeldrifts. Federated adaptations of regular Knowledge Distillation (KD) can solveand/or mitigate the weaknesses of parameter-averaging FL algorithms whilepossibly introducing other trade-offs. In this article, we provide a review ofKD-based algorithms tailored for specific FL issues.

Knowledge Distillation for Federated Learning: a Practical Guide