Welcome to the resource topic for 2022/1695
Title:
ELSA: Secure Aggregation for Federated Learning with Malicious Actors
Authors: Mayank Rathee, Conghao Shen, Sameer Wagh, Raluca Ada Popa
Abstract:Federated learning (FL) is an increasingly popular
approach for machine learning (ML) in cases where the train-
ing dataset is highly distributed. Clients perform local training
on their datasets and the updates are then aggregated into
the global model. Existing protocols for aggregation are either
inefficient, or don’t consider the case of malicious actors in the
system. This is a major barrier in making FL an ideal solution
for privacy-sensitive ML applications. We present ELSA, a
secure aggregation protocol for FL, which breaks this barrier -
it is efficient and addresses the existence of malicious actors at
the core of its design. Similar to prior work on Prio and Prio+,
ELSA provides a novel secure aggregation protocol built out of
distributed trust across two servers that keeps individual client
updates private as long as one server is honest, defends against
malicious clients and is efficient end-to-end. Compared to prior
works, the distinguishing theme in ELSA is that instead of the
servers generating cryptographic correlations interactively, the
clients act as untrusted dealers of these correlations without
compromising the protocol’s security. This leads to a much
faster protocol while also achieving stronger security at that ef-
ficiency compared to prior work. We introduce new techniques
that retain privacy even when a server is malicious at a small
added cost of 7-25% in runtime with negligible increase in
communication over the case of semi-honest server. Our work
improves end-to-end runtime over prior work with similar
security guarantees by big margins - single-aggregator RoFL
by up to 305x (for the models we consider), and distributed
trust Prio by up to 8x
ePrint: https://eprint.iacr.org/2022/1695
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