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In this paper, we address the challenges of asynchronous gradient descent in distributed learning environments, particularly focusing on addressing the challenges of stale gradients and the need for extensive communication resources. We develop a novel communication efficient framework that incorporates a gradient evaluation algorithm to assess and utilize delayed gradients based on their quality, ensuring efficient and effective model updates while significantly reducing communication overhead. Our proposed algorithm requires agents to only send the norm of the gradients rather than the computed gradient. The server then decides whether to accept the gradient if the ratio between the norm of the gradient and the distance between the global model parameter and the local model parameter exceeds a certain threshold. With the proper choice of the threshold, we show that the convergence rate achieves the same order as the synchronous stochastic gradient without depending on the staleness value unlike most of the existing works. Given the computational complexity of the initial algorithm, we introduce a simplified variant that prioritizes the practical applicability without compromising on the convergence rates. Our simulations demonstrate that our proposed algorithms outperform existing state-of-the-art methods, offering improved convergence rates, stability, accuracy, and resource consumption. 

This study demonstrates that a dialkyl ditelluride reagent can produce metastable and difficult-to-achieve metal telluride phases in nanocrystal syntheses. Using didodecyl ditelluride and without the need for phosphine precursors, nanocubes of the pseudo-cubic phase (Cu 1.5 Te) were synthesized at the moderate temperature of 135 °C. At the higher temperature of 155 °C, 2-D nanosheets of vulcanite (CuTe) resulted, a nanomaterial in a phase that has not been previously achieved through thermal decomposition methods. Materials were characterized with TEM, powder XRD and UV-Vis-NIR absorbance spectroscopy.