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Nov 26, 2023 · In this paper, we focus on examining the interplay among key factors in the widely used distributed Stochastic Gradient Descent (SGD) algorithm ...
In this paper, we focus on examining the interplay among key factors in the widely used distributed Stochastic Gradient Descent (SGD) algorithm with ...
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This paper investigates optimal implementations of federated learning (FL) in practical edge computing systems with possibly distinct computing and ...
Sep 30, 2024 · To reduce communication costs, FedAvg allows mul- tiple SGD steps at each client before exchanging the model, proving effective in real-world ...
QSGD guarantees convergence for convex and non-convex objectives, under asynchrony, and can be extended to stochastic variance-reduced techniques. When applied ...
Sep 6, 2024 · In this paper, we propose Quantized SGD (QSGD), a family of compression schemes which allow the compression of gradient updates at each node, ...
Jun 13, 2023 · In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform ...
In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform ...
Feb 1, 2024 · We propose an adaptive gradient quantization approach that enhances communication efficiency. Aiming to minimize the total communication costs.
This article proposes a communication-efficient FL framework with an Adaptive Quantized Gradient (AQG), which adaptively adjusts the quantization level.