Jul 10, 2024 · In this paper, we propose a trustworthy distributed machine learning (TDML) framework that leverages blockchain to coordinate remote trainers and validate ...
Jul 11, 2024 · Device-to-Device(D2D) communication technology has been proposed to perfectly solve this type of problem. The idle resources available in the proximity are used ...
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Jul 7, 2024 · In this paper, we propose a distributed federated learning framework for IoT devices, more specifically for IoMT (Internet of Medical Things), using ...
1 day ago · This survey explores recent advancements in training systems for LLMs, including innovations in training infrastructure with AI accelerators, networking, ...
Jul 5, 2024 · It demonstrates the extent to which enjoyment, the most observable positive emotion in L2 learning, influences their involvement in AI-mediated IDLE (AI-IDLE) ...
Jul 10, 2024 · Specifically, we propose two efficient activation rematerial- ization strategies: Pipeline-Parallel-Aware Offloading, which maximizes the utilization of host ...
4 days ago · It aims at integrating and scheduling offline idle resources through online recruitment, and to achieve efficient sharing of idle resources [3]. In the SC ...
Jul 10, 2024 · In this paper, we present Metis, a system designed to auto- mate distributed training on a variety of GPU types. Our key design principle in tackling the first ...
Jul 11, 2024 · Federated learning is a special kind of distributed learning framework, which allows multiple users to participate in model training while ensuring that their ...