Nov 6, 2023 · This paper delves into the challenges of training AI models across geographically distributed (geo-distributed) data centers, emphasizing the ...
May 31, 2024 · This paper delves into the challenges of training AI models across geographically distributed (geo-distributed) data centers, emphasizing the ...
This paper delves into the challenges of training AI models across geographically distributed (geo-distributed) data centers, emphasizing the balance between ...
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers. J Bian, L Wang, S Ren, J Xu. The 15th ACM International Conference on Future ...
In this paper, we propose an online algorithm, called COCA (optimizing for COst minimization and CArbon neutrality), for minimizing data center operational cost ...
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Ren, J. Xu, "CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers," The 15th ACM International Conference on Future and Sustainable ...
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers ... data centers, emphasizing the balance between learning performance and carbon ...
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers, Jieming Bian et.al. 2311.03615v1, null. 2023-11-06, Asynchronous Local ...
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers. J Bian, L Wang, S Ren, J Xu. The 15th ACM International Conference on Future ...
May 14, 2024 · Federated Learning (FL) is a distributed machine learning paradigm that allows clients to train models on their data while preserving their ...