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Found 3 papers, 2 papers with code
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees
Existing DFL works have mostly focused on settings where clients conduct a fixed number of local updates between local model exchanges, overlooking heterogeneity and dynamics in communication and computation capabilities.
The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks
This paper addresses this gap by analyzing the performance of decentralized FL for various adversarial placement strategies when adversaries can jointly coordinate their placement within a network.
On the Effects of Data Heterogeneity on the Convergence Rates of Distributed Linear System Solvers
Using this notion, we bound and compare the convergence rates of the studied algorithms and capture the effects of both cross-machine and local data heterogeneity on these quantities.
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