AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A new approach, FedLR, is proposed in this manuscript to remedy the problem of slacker devices, which provides all the benefits of FedAvg while reducing the communication costs between the devices.
An asynchronous framework named FedQS, which employs a multi-dimensional staleness evaluation mechanism that dynamically assesses updates by combining the similarity between local and global models with client latency metrics, and implements a decoupling solution via a queue scheduling algorithm to resolve the coupling...
This paper proposes a selection strategy according to data heterogeneity, bandwidth and system constraints, and recommends the unified reporting of accuracy, communication volume, communication rounds, time, and energy consumption.
Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server. Straggling clients have been a problem for FL as they introduce delays in aggregating the local models and hence, the convergence of the global model. Ther...
With the rapid advancement of the Internet and the emergence and popularity of large language models, the demands for high-quality data, communication capacity and security and privacy have increased significantly. However, a large amount of data is distributed across different devices, and cannot be collected legally...
Federated learning (FL) offers an appealing way to train models across distributed devices without centralizing raw data, but deploying it in large, real-world networks quickly reveals serious limitations. Communication bottlenecks, unreliable connections, and the risk of a single aggregation server becoming a point of...
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to track computational performance. The post Broadening access to Skala creates a faster path to predictive DFT appeared first on Microsoft Research.
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