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Found 5 papers, 0 papers with code
Fair Concurrent Training of Multiple Models in Federated Learning
We show how our fairness-based learning and incentive mechanisms impact training convergence and finally evaluate our algorithm with multiple sets of learning tasks on real world datasets.
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.
Robust Regression via Model Based Methods
The mean squared error loss is widely used in many applications, including auto-encoders, multi-target regression, and matrix factorization, to name a few.
Rate Allocation and Content Placement in Cache Networks
We introduce the problem of optimal congestion control in cache networks, whereby \emph{both} rate allocations and content placements are optimized \emph{jointly}.
Networking and Internet Architecture
