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2017

Differentially Private Federated Learning: A Client Level Perspective

Geyer, Robin C., Klein, Tassilo, Nabi, Moin

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Federated learning is a recent advance in privacy protection.

In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients.
The resulting model is then distributed back to all clients, ultimately converging to a joint representative model without explicitly having to share the data.
However, the protocol is vulnerable to differential attacks, which could originate from any party contributing during federated optimization.

Differentially Private Federated Learning: A Client Level Perspective · Around

Built on

Differential privacy
C. Dwork · 2006
Earlier work this paper cites.
Wherefore art thou r3579x?: Anonymized social networks, hidden patterns, and structural steganography
L. Backstrom, C. Dwork, and J. Kleinberg · 2007
Earlier work this paper cites.
Robust de-anonymization of large sparse datasets
A. Narayanan and V. Shmatikov · 2008
Earlier work this paper cites.

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The algorithmic foundations of differential privacy
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Communication-Efficient Learning of Deep Networks from Decentralized Data
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Learning differentially private language models without losing accuracy
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