41 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
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem.
CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers.
We characterize CodedPrivateML\u27s privacy threshold and prove its convergence for logistic (and linear) regression. Furthermore, via experiments over Amazon EC2, we demonstrate that CodedPrivateML can provide an order of magnitude speedup (up to ) over the state-of-the-art cryptographic approaches
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
Secure aggregation is a critical component in federated learning, which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of federated learning. In fact, we empirically show that the conventional random user selection strategies for federated learning lead to leaking users\u27 individual models within number of rounds linear in the number of users. To address this challenge, we introduce a secure aggregation framework with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of federated learning over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. We perform several experiments on MNIST and CIFAR-10 datasets in the IID and the non-IID settings to demonstrate the performance improvement over the baseline algorithms, both in terms of privacy protection and test accuracy
Q^2 Dependence of the S_{11}(1535) Photocoupling and Evidence for a P-wave resonance in eta electroproduction
New cross sections for the reaction are reported for total
center of mass energy =1.5--2.3 GeV and invariant squared momentum transfer
=0.13--3.3 GeV. This large kinematic range allows extraction of new
information about response functions, photocouplings, and coupling
strengths of baryon resonances. A sharp structure is seen at 1.7 GeV.
The shape of the differential cross section is indicative of the presence of a
-wave resonance that persists to high . Improved values are derived for
the photon coupling amplitude for the (1535) resonance. The new data
greatly expands the range covered and an interpretation of all data with
a consistent parameterization is provided.Comment: 31 pages, 9 figure