Federated learning (FL) provides a promising collaborative framework to build
a model from distributed clients, and this work investigates the carbon
emission of the FL process. Cloud and edge servers hosting FL clients may
exhibit diverse carbon footprints influenced by their geographical locations
with varying power sources, offering opportunities to reduce carbon emissions
by training local models with adaptive computations and communications. In this
paper, we propose FedGreen, a carbon-aware FL approach to efficiently train
models by adopting adaptive model sizes shared with clients based on their
carbon profiles and locations using ordered dropout as a model compression
technique. We theoretically analyze the trade-offs between the produced carbon
emissions and the convergence accuracy, considering the carbon intensity
discrepancy across countries to choose the parameters optimally. Empirical
studies show that FedGreen can substantially reduce the carbon footprints of FL
compared to the state-of-the-art while maintaining competitive model accuracy.

FedGreen 是一种碳感知的联邦学习方法，通过采用基于客户端的碳排放数据和位置的自适应模型大小训练技巧，以及使用有序的随机失活作为模型压缩技术，可以有效地训练模型，并在考虑不同国家碳强度差异的同时，降低碳排放，并保持竞争性的模型准确性。

FedGreen：具有模型大小自适应的碳感知联邦学习

FedGreen: Carbon-aware Federated Learning with Model Size Adaptation

A large-scale dynamic network (LDN) is a source of data in many big
data-related applications due to their large number of entities and large-scale
dynamic interactions. They can be modeled as a high-dimensional incomplete
(HDI) tensor that contains a wealth of knowledge about time patterns. A Latent
factorization of tensors (LFT) model efficiently extracts this time pattern,
which can be established using stochastic gradient descent (SGD) solvers.
However, LFT models based on SGD are often limited by training schemes and have
poor tail convergence. To solve this problem, this paper proposes a novel
nonlinear LFT model (MNNL) based on momentum-incorporated SGD, which extracts
non-negative latent factors from HDI tensors to make training unconstrained and
compatible with general training schemes, while improving convergence accuracy
and speed. Empirical studies on two LDN datasets show that compared to existing
models, the MNNL model has higher prediction accuracy and convergence speed.

本文提出了一种基于动量梯度下降的非线性潜在张量因子分解模型 (MNNL)，可从高维不完整张量 (HDI) 中提取非负潜在因子，改善了传统 LFT 模型的训练问题，提高了预测准确性和收敛速度。