FederatedTree: A Secure Serverless Algorithm for Federated Learning to Reduce Data Leakage

In Federated Learning there have been many op-timization methods that allow flexible local updating such as FedAvg that has become the de facto mechanism for averaging local stochastic gradient descent without sharing the data. Classic FL methods such as FedAvg struggle with trust and data leakage issues. In FedAvg and similar techniques, clients assume the aggregator server is a trusted but curious server. However, even if the server is trusted, the models still leak a lot of data through the weights. Several techniques have been proposed to reduce data leakage. One mechanism involves sharing pieces of the data with the server, but it violates the key privacy assumption of federated learning. Other solutions such as Federated Learning with Differential Privacy aim to reduce data leakage by adding noise to the weights/gradients. However, there is a trade-off between accuracy and the amount of noise added.In this paper, we propose a practical Federated Learning algorithm of deep neural networks on iterative model averaging we called FederatedTree. While FedAvg with differential privacy adds noise to the weights to provide a level of privacy, our algorithm applies a secure sequential averaging without adding noise to the models. FederatedTree solves the trust issue between client-to-client, client-to-server (if exists) and reduces the amount of data leakage without adding noise that lowers the model accuracy. The results show that the FederatedTree algorithm provides a high privacy rate with higher accuracy on popular datasets: MNIST, Fashion MNIST, CIFAR-10. Furthermore, FederatedTree utilizes a binary tree structure to reduce the sequential averaging time and remove the overhead of the excessive communication between the server and the clients.