Federated Learning (FL) enables multiple parties to distributively train a ML model without revealing their private datasets. However, it assumes trust in the centralized aggregator which stores and aggregates model updates. This makes it prone to gradient tampering and privacy leakage by a malicious aggregator. Malicious parties can also introduce backdoors into the joint model by poisoning the training data or model gradients. To address these issues, we present BEAS, the first blockchain-based framework for N-party FL that provides strict privacy guarantees of training data using gradient pruning (showing improved differential privacy compared to existing noise and clipping based techniques). Anomaly detection protocols are used to minimize the risk of data-poisoning attacks, along with gradient pruning that is further used to limit the efficacy of model-poisoning attacks. We also define a novel protocol to prevent premature convergence in heterogeneous learning environments. We perform extensive experiments on multiple datasets with promising results: BEAS successfully prevents privacy leakage from dataset reconstruction attacks, and minimizes the efficacy of poisoning attacks. Moreover, it achieves an accuracy similar to centralized frameworks, and its communication and computation overheads scale linearly with the number of participants.

BEAS 是第一个基于区块链的 N-party Federated Learning 框架，使用梯度剪枝技术提供严格的隐私保障，减少了数据投毒和模型投毒攻击，并采用一种新的协议来防止异构学习环境下的早期收敛。在多个数据集上进行了广泛的实验，显示出与集中式框架相似的准确性和线性的通信和计算成本。

BEAS：区块链支持的异步安全联邦机器学习