We consider the problem of training a machine learning model over a network of users in a fully decentralized framework. The users take a Bayesian-like approach via the introduction …
… Abstract—In recent years, FederatedLearning (FL) has gained relevance in … Decentralized FederatedLearning (DFL) emerged to address these concerns by promoting decentralized …
H Ye, L Liang, GY Li - IEEE journal of selected topics in signal …, 2022 - ieeexplore.ieee.org
… Traditional federatedlearning is designed with a parameter… the decentralized training paradigm building on the device-to-device (D2D) network, which facilitates collaborative learning …
C Hu, J Jiang, Z Wang - arXiv preprint arXiv:1908.07782, 2019 - arxiv.org
… In real-world federatedlearning scenarios, the network ca… is a novel decentralized federated learning design, introducing … Second, we propose a decentralizedfederatedlearning design…
… -learning model without sharing their data. However, the majority of the existing federated … In this work, we introduce IPLS, a fully decentralizedfederatedlearning framework that is …
… However, the security of federatedlearning is increasingly being … a decentralizedfederated learning framework based on blockchain, that is, a Blockchain-based FederatedLearning …
W Liu, L Chen, W Zhang - IEEE Transactions on Signal and …, 2022 - ieeexplore.ieee.org
… Abstract—Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federatedlearning (DFL). The performance of decentralized SGD is jointly influenced by …
… as managing heterogeneous federation network topologies, … of metrics to evaluate different federation scenarios for efficient … train FL models in a decentralized, semi-decentralized, and …
… Gossip learning is a decentralized alternative to federatedlearning that does not require an … The natural hypothesis is that gossip learning is strictly less efficient than federatedlearning …