2,949 research outputs found
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
Federated learning is a distributed framework for training machine learning
models over the data residing at mobile devices, while protecting the privacy
of individual users. A major bottleneck in scaling federated learning to a
large number of users is the overhead of secure model aggregation across many
users. In particular, the overhead of the state-of-the-art protocols for secure
model aggregation grows quadratically with the number of users. In this paper,
we propose the first secure aggregation framework, named Turbo-Aggregate, that
in a network with users achieves a secure aggregation overhead of
, as opposed to , while tolerating up to a user dropout
rate of . Turbo-Aggregate employs a multi-group circular strategy for
efficient model aggregation, and leverages additive secret sharing and novel
coding techniques for injecting aggregation redundancy in order to handle user
dropouts while guaranteeing user privacy. We experimentally demonstrate that
Turbo-Aggregate achieves a total running time that grows almost linear in the
number of users, and provides up to speedup over the
state-of-the-art protocols with up to users. Our experiments also
demonstrate the impact of model size and bandwidth on the performance of
Turbo-Aggregate
Glimmers: Resolving the Privacy/Trust Quagmire
Many successful services rely on trustworthy contributions from users. To
establish that trust, such services often require access to privacy-sensitive
information from users, thus creating a conflict between privacy and trust.
Although it is likely impractical to expect both absolute privacy and
trustworthiness at the same time, we argue that the current state of things,
where individual privacy is usually sacrificed at the altar of trustworthy
services, can be improved with a pragmatic , which allows
services to validate user contributions in a trustworthy way without forfeiting
user privacy. We describe how trustworthy hardware such as Intel's SGX can be
used client-side -- in contrast to much recent work exploring SGX in cloud
services -- to realize the Glimmer architecture, and demonstrate how this
realization is able to resolve the tension between privacy and trust in a
variety of cases
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