On Privacy and Personalization in Cross-Silo Federated Learning. (arXiv:2206.07902v1 [cs.LG])

While the application of differential privacy (DP) has been well-studied in
cross-device federated learning (FL), there is a lack of work considering DP
for cross-silo FL, a setting characterized by a limited number of clients each
containing many data subjects. In cross-silo FL, usual notions of client-level
privacy are less suitable as real-world privacy regulations typically concern
in-silo data subjects rather than the silos themselves. In this work, we
instead consider the more realistic notion of silo-specific item-level privacy,
where …

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