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Found 6 papers, 1 papers with code
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
It consists of a parameter server and a possibly large collection of clients (e. g., in cross-device federated learning) that may operate in congested and changing environments.
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications
Specifically, in each round $t$, the link between the PS and client $i$ is active with probability $p_i^t$, which is $\textit{unknown}$ to both the PS and the clients.
Federated Learning in the Presence of Adversarial Client Unavailability
Federated learning is a decentralized machine learning framework that enables collaborative model training without revealing raw data.
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms
Federated Learning (FL) is a nascent decentralized learning framework under which a massive collection of heterogeneous clients collaboratively train a model without revealing their local data.
An Empirical Analysis of Approximation Algorithms for the Euclidean Traveling Salesman Problem
We evaluate greedy, 2-opt, and genetic algorithms.
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