4,945 research outputs found
Federated Neural Architecture Search
To preserve user privacy while enabling mobile intelligence, techniques have
been proposed to train deep neural networks on decentralized data. However,
training over decentralized data makes the design of neural architecture quite
difficult as it already was. Such difficulty is further amplified when
designing and deploying different neural architectures for heterogeneous mobile
platforms. In this work, we propose an automatic neural architecture search
into the decentralized training, as a new DNN training paradigm called
Federated Neural Architecture Search, namely federated NAS. To deal with the
primary challenge of limited on-client computational and communication
resources, we present FedNAS, a highly optimized framework for efficient
federated NAS. FedNAS fully exploits the key opportunity of insufficient model
candidate re-training during the architecture search process, and incorporates
three key optimizations: parallel candidates training on partial clients, early
dropping candidates with inferior performance, and dynamic round numbers.
Tested on large-scale datasets and typical CNN architectures, FedNAS achieves
comparable model accuracy as state-of-the-art NAS algorithm that trains models
with centralized data, and also reduces the client cost by up to two orders of
magnitude compared to a straightforward design of federated NAS
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
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