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Found 6 papers, 1 papers with code
Biased Over-the-Air Federated Learning under Wireless Heterogeneity
Recently, Over-the-Air (OTA) computation has emerged as a promising federated learning (FL) paradigm that leverages the waveform superposition properties of the wireless channel to realize fast model updates.
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach
Focusing on a single FL round, we cast the optimal scheduling problem as the minimization of the mean squared error (MSE) on the estimated global gradient at the PS, subject to a delay constraint, yielding the optimal device scheduling configuration and quantization bits for the digital devices.
Delay-Aware Hierarchical Federated Learning
The paper introduces delay-aware hierarchical federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model training by accounting for communication delays between edge and cloud.
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling
Federated learning has emerged as a popular technique for distributing model training across the network edge.
Finite-Bit Quantization For Distributed Algorithms With Linear Convergence
This paper studies distributed algorithms for (strongly convex) composite optimization problems over mesh networks, subject to quantized communications.
Federated Learning with Communication Delay in Edge Networks
Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks.
