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Federated learning enables a large number of clients to participate in learning a shared model while maintaining the training data stored in each client, which protects data privacy and security. Till now, federated learning frameworks are built in a centralized way, in which a central client is needed for collecting and distributing information from every other client. This not only leads to high communication pressure at the central client, but also renders the central client highly vulnerable to failure and attack. Here we propose a principled decentralized federated learning algorithm (DeFed), which removes the central client in the classical Federated Averaging (FedAvg) setting and only relies information transmission between clients and their local neighbors. The proposed DeFed algorithm is proven to reach the global minimum with a convergence rate of $O(1/T)$ when the loss function is smooth and strongly convex, where $T$ is the number of iterations in gradient descent. Finally, the proposed algorithm has been applied to a number of toy examples to demonstrate its effectiveness.
In this paper, we focus on facilitating differentially private quantized communication between the clients and server in federated learning (FL). Towards this end, we propose to have the clients send a textit{private quantized} version of only the te
Decentralized federated learning (DFL) is a powerful framework of distributed machine learning and decentralized stochastic gradient descent (SGD) is a driving engine for DFL. The performance of decentralized SGD is jointly influenced by communicatio
Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XGBoost, the state-of-the-art tree boosting framework, to federated lear
Federated learning (FL) is an emerging paradigm for machine learning, in which data owners can collaboratively train a model by sharing gradients instead of their raw data. Two fundamental research problems in FL are incentive mechanism and privacy p
We present a novel privacy preservation strategy for decentralized visualization. The key idea is to imitate the flowchart of the federated learning framework, and reformulate the visualization process within a federated infrastructure. The federatio