ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Federated Learning Networks
Niousha Nazemi, Omid Tavallaie, Shuaijun Chen, Anna Maria Mandalari,, Kanchana Thilakarathna, Ralph Holz, Hamed Haddadi, Albert Y. Zomaya

TL;DR
ACCESS-FL introduces a novel secure aggregation method for federated learning that significantly reduces communication and computation costs in honest-but-curious scenarios, especially with limited client dropouts.
Contribution
The paper proposes ACCESS-FL, a communication-and-computation-efficient secure aggregation protocol that eliminates cryptography overhead by sharing secrets between only two clients, suitable for stable FL networks.
Findings
Reduces computation and communication overhead compared to SecAgg and SecAgg+
Maintains security in honest-but-curious scenarios with limited client dropouts
Effective on datasets like MNIST, FMNIST, and CIFAR
Abstract
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data and generates a global model on a server by aggregating model updates. Traditional FL approaches risk exposing sensitive client data when plain model updates are transmitted to the server, making them vulnerable to security threats such as model inversion attacks where the server can infer the client's original training data from monitoring the changes of the trained model in different rounds. Google's Secure Aggregation (SecAgg) protocol addresses this threat by employing a double-masking technique, secret sharing, and cryptography computations in honest-but-curious and adversarial scenarios with client dropouts. However, in scenarios without the presence of an active adversary, the computational and…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques · Cryptography and Data Security
