SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation
Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets. However, a malicious aggregation server might use the model parameters to derive sensitive information about the training dataset used. To address such leakage, differential privacy and cryptographic techniques have been investigated in prior work, but these often result in large communication overheads or impact model performance. To mitigate this centralization of power, we propose SCOTCH, a decentralized m-party secure-computation framework for federated aggregation that deploys MPC primitives, such as secret sharing. Our protocol is simple, efficient, and provides strict privacy guarantees against curious aggregators or colluding data-owners with minimal communication overheads compared to other existing state-of-the-art privacy-preserving federated learning frameworks. We evaluate our framework by performing extensive experiments on multiple datasets with promising results. SCOTCH can train the standard MLP NN with the training dataset split amongst 3 participating users and 3 aggregating servers with 96.57% accuracy on MNIST, and 98.40% accuracy on the Extended MNIST (digits) dataset, while providing various optimizations.
Code (1)
Tasks
Federated LearningPrivacy PreservingSimilar Papers 제목 키워드 기반
Sparsified Secure Aggregation for Privacy-Preserving Federated Learning
Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear. On the other hand, conventional secure aggregation p…
Federated LearningPrivacy PreservingSecure Byzantine-Robust Distributed Learning via Clustering
Federated learning systems that jointly preserve Byzantine robustness and privacy have remained an open problem. Robust aggregation, the standard defense for Byzantine attacks, generally requires server access to individ…
ClusteringFederated LearningPrivacy PreservingInformation-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries
On-device federated learning (FL) enables privacy-preserving and personalized model training on resource-constrained devices such as smartphones and IoT nodes. To reduce communication cost, sign-based methods (e.g., sign…
Federated LearningSecure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data
Federated learning enables multiple institutions to train shared models without exchanging raw clinical EEG data, but it does not fully prevent privacy leakage from individual model updates. This paper presents a privacy…
Federated LearningEluding Secure Aggregation in Federated Learning via Model Inconsistency
Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents th…
Federated Learning