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 $N$ users achieves a secure aggregation overhead of $O(N\log{N})$, as opposed to $O(N^2)$, while tolerating up to a user dropout rate of $50\%$. 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 $40\times$ speedup over the state-of-the-art protocols with up to $N=200$ 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 $\sim 34\times$) over the state-of-the-art cryptographic approaches

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 $ep \to e'\eta p$ are reported for total center of mass energy $W$=1.5--2.3 GeV and invariant squared momentum transfer $Q^2$=0.13--3.3 GeV$^2$. This large kinematic range allows extraction of new information about response functions, photocouplings, and $\eta N$ coupling strengths of baryon resonances. A sharp structure is seen at $W\sim$ 1.7 GeV. The shape of the differential cross section is indicative of the presence of a $P$-wave resonance that persists to high $Q^2$. Improved values are derived for the photon coupling amplitude for the $S_{11}$(1535) resonance. The new data greatly expands the $Q^2$ range covered and an interpretation of all data with a consistent parameterization is provided.Comment: 31 pages, 9 figure