TL;DR
Flamingo is a novel multi-round secure aggregation system for federated learning that reduces interaction complexity and maintains model accuracy while ensuring privacy of client data.
Contribution
It introduces a multi-round secure aggregation protocol with dropout resilience and local client neighborhood selection, improving efficiency over previous single-round protocols.
Findings
Successfully trains neural networks on MNIST and CIFAR-100 datasets.
Achieves secure aggregation with no loss in model accuracy.
Reduces interaction rounds and runtime compared to prior methods.
Abstract
This paper introduces Flamingo, a system for secure aggregation of data across a large set of clients. In secure aggregation, a server sums up the private inputs of clients and obtains the result without learning anything about the individual inputs beyond what is implied by the final sum. Flamingo focuses on the multi-round setting found in federated learning in which many consecutive summations (averages) of model weights are performed to derive a good model. Previous protocols, such as Bell et al. (CCS '20), have been designed for a single round and are adapted to the federated learning setting by repeating the protocol multiple times. Flamingo eliminates the need for the per-round setup of previous protocols, and has a new lightweight dropout resilience protocol to ensure that if clients leave in the middle of a sum the server can still obtain a meaningful result. Furthermore,…
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Taxonomy
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