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Federated learning (FL) is an increasingly popular approach for machine learning (ML) when the training dataset is highly distributed. Clients perform local training on their datasets and the updates are then aggregated into the global model. Existing protocols for aggregation are either inefficient or don’t consider the case of malicious actors in the system. This is a major barrier to making FL an ideal solution for privacy-sensitive ML applications. In this talk, I will present ELSA, a secure aggregation protocol for FL that breaks this barrier - it is efficient and addresses the existence of malicious actors (clients + servers) at the core of its design. Similar to prior work Prio and Prio+, ELSA provides a novel secure aggregation protocol built out of distributed trust across two servers that keeps individual client updates private as long as one server is honest, defends against malicious clients, and is efficient end-to-end. Compared to prior works, the distinguishing theme in ELSA is that instead of the servers generating cryptographic correlations interactively, the clients act as untrusted dealers of these correlations without compromising the protocol’s security. This leads to a much faster protocol while also achieving stronger security at that efficiency compared to prior work. We introduce new techniques that retain privacy even when a server is malicious at a small added cost of 7-25% in runtime with a negligible increase in communication over the case of a semi-honest server. ELSA improves end-to-end runtime over prior work with similar security guarantees by big margins - single-aggregator RoFL by up to 305x (for the models we consider), and distributed-trust Prio by up to 8x (with up to 16x faster server-side protocol). Additionally, ELSA can be run in a bandwidth-saver mode for clients who are geographically bandwidth-constrained - an important property that is missing from prior works.
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Improving Federated Learning Security with Trust Evaluation to Detect Adversarial Attacks”
Federated Learning (FL), an emerging decentralized Machine Learning (ML) approach, offers a promising avenue for training models on distributed data while safeguarding individual privacy. Nevertheless, when imple- mented in real ML applications, adversarial attacks that aim to deteriorate the quality of the local training data and to compromise the performance of the resulting model still remaining a challenge. In this paper, we propose and develop an approach that integrates Reputation and Trust techniques into the conventional FL. These techniques incur a novel local models’ pre-processing step performed before the aggregation procedure, in which we cluster the local model updates in their parameter space and employ clustering results to evaluate trust towards each of the local clients. The trust value is updated in each aggregation round, and takes into account retrospective evaluations performed in the previous rounds that allow considering the history of updates to make the assessment more informative and reliable. Through our empirical study on a traffic signs classification computer vision application, we verify our novel approach that allow to identify local clients compromised by adversarial attacks and submitting updates detrimental to the FL performance. The local updates provided by non-trusted clients are excluded from aggregation, which allows to enhance FL security and robustness to the models that might be trained on corrupted data.
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- Award ID(s):
- 2321652
- PAR ID:
- 10532526
- Editor(s):
- Goel, S
- Publisher / Repository:
- 19th Annual Symposium on Information Assurance (ASIA’ 24) , June 4-5, 2024, Albany, NY
- Date Published:
- Format(s):
- Medium: X
- Location:
- Albany, NY
- Sponsoring Org:
- National Science Foundation
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Federated learning (FL) is an increasingly popular approach for machine learning (ML) in cases where the training dataset is highly distributed. Clients perform local training on their datasets and the updates are then aggregated into the global model. Existing protocols for aggregation are either inefficient, or don’t consider the case of malicious actors in the system. This is a major barrier in making FL an ideal solution for privacy-sensitive ML applications. We present ELSA, a secure aggregation protocol for FL, which breaks this barrier - it is efficient and addresses the existence of malicious actors at the core of its design. Similar to prior work on Prio and Prio+, ELSA provides a novel secure aggregation protocol built out of distributed trust across two servers that keeps individual client updates private as long as one server is honest, defends against malicious clients, and is efficient end-to-end. Compared to prior works, the distinguishing theme in ELSA is that instead of the servers generating cryptographic correlations interactively, the clients act as untrusted dealers of these correlations without compromising the protocol’s security. This leads to a much faster protocol while also achieving stronger security at that efficiency compared to prior work. We introduce new techniques that retain privacy even when a server is malicious at a small added cost of 7-25% in runtime with negligible increase in communication over the case of semi-honest server. Our work improves end-to-end runtime over prior work with similar security guarantees by big margins - single-aggregator RoFL by up to 305x (for the models we consider), and distributed trust Prio by up to 8x.more » « less
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