Training large-scale Neural Networks requires substantial computational power and energy. Federated Learning enables distributed model training across geospatially distributed data centers, leveraging renewable energy sources to reduce the carbon footprint of AI training. Various client selection strategies have been developed to align the volatility of renewable energy with stable and fair model training in a federated system. However, due to the privacy-preserving nature of Federated Learning, the quality of data on client devices remains unknown, posing challenges for effective model training. In this paper, we introduce a modular approach on top to state-of-the-art client selection strategies for carbon-efficient Federated Learning. Our method enhances robustness by incorporating a noisy client data filtering, improving both model performance and sustainability in scenarios with unknown data quality. Additionally, we explore the impact of carbon budgets on model convergence, balancing efficiency and sustainability. Through extensive evaluations, we demonstrate that modern client selection strategies based on local client loss tend to select clients with noisy data, ultimately degrading model performance. To address this, we propose a gradient norm thresholding mechanism using probing rounds for more effective client selection and noise detection, contributing to the practical deployment of carbon-efficient Federated Learning.
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Figure 1. System Overview: Clients represent datacenters in 30 regions within the US. While the data quality is unknown we introduce probing rounds in the beginning of federated training to identify clients with noisy data. Integrating carbon budgets we can balance clients with high data utility and their current carbon intensity.
Figure 2. Assigned carbon intensity values for clients over 100 training rounds. Each round corresponds to one hour, spanning from 2023-01-15 00:00:00 to 2023-01-19 04:00:00 (UTC). The bottom plot highlights daytime intensity patterns for three clients from different regions.
Figure 3. Client selection counts, 6 out of 30 clients contain noisy data.
Figure 4. Convergence curves under noisy data scenario. Baseline methods are compared with their thresholded variants. Thresholding reduces the impact of corrupted clients, resulting in faster and more stable convergence.
Figure 5. Accuracy–emissions comparison across methods. Bars show the carbon emissions spent during training up to the point of maximum accuracy, while the line shows the corresponding maximum accuracy.
Figure 6. Impact of carbon budgeting on CO 2 -emissions and model performance, compared to standard Oort client selection with full client availability. Results are shown for two scenarios: (a) clients with clean data, and (b) clients with corrupted data.
Figure 7. Client selection during training: Comparison between two approaches—one with unconstrained emissions allowing full client participation in each round, and another enforcing a hard constraint where only zero-emission clients are selected. A third, more flexible strategy, balancing carbon budgeting with broader client availability during training.
Figure 8. Impact of carbon budgeting on CO 2 -emissions and model performance using the DenseNet-121 model evaluated on the CIFAR-100 dataset and the EfficientNet-B1 model evaluated on the Tiny ImageNet dataset. Results are shown for two scenarios: clients with clean data and corrupted data, where noise was added to 6 out of 30 clients.
Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the presence of non-IID data, which negatively impacts convergence speed and final model accuracy. Conventional alternatives suffer from significant inefficiency. Clients with noisy or highly heterogeneous data contribute expensive gradient computations that are either discarded or heavily down-weighted before aggregation. These reactive approaches waste computational resources, require more communication rounds and result in unnecessary privacy exposure. In this paper, we propose a proactive client selection framework that aims to find an optimal federation of clients whose combined data match utility and fairness requirements before training begins. Our method relies on mutual information computed from differentially private contingency tables to quantify the relevance of cross-feature correlations in the union dataset. We introduce a Potential Federation Loss (PFL) over the set of fixed-size federations, which balances two objectives. Maximizing collective data utility while ensuring fair cross-features correlations to prevent group unfairness. Client selection is expressed as an optimal subset search problem over the PFL objective, which we solve using simulated annealing under strong differential privacy guarantees for clients' local statistics. Experimental results on four benchmarks show faster, fairer, and more accurate models trained on optimally found federations, compared to uniform sampling, even when state-of-the-art adaptive aggregation or sampling strategies are employed.
Federated Learning (FL) enables a distributed client-server architecture where multiple clients collaboratively train a global Machine Learning (ML) model without sharing sensitive local data. However, FL often results in lower accuracy than traditional ML algorithms due to statistical heterogeneity across clients. Prior works attempt to address this by using model updates, such as loss and bias, from client models to select participants that can improve the global model's accuracy. However, these updates neither accurately represent a client's heterogeneity nor are their selection methods deterministic. We mitigate these limitations by introducing Terraform, a novel client selection methodology that uses gradient updates and a deterministic selection algorithm to select heterogeneous clients for retraining. This bi-pronged approach allows Terraform to achieve up to 47 percent higher accuracy over prior works. We further demonstrate its efficiency through comprehensive ablation studies and training time analyses, providing strong justification for the robustness of Terraform.
Federated Learning (FL) enables distributed Artificial Intelligence (AI) across cloud-edge environments by allowing collaborative model training without centralizing data. In cross-device deployments, FL systems face strict communication and participation constraints, as well as strong non-independent and identically distributed (non-IID) data that degrades convergence and model quality. Since only a subset of devices (a.k.a clients) can participate per training round, intelligent client selection becomes a key systems challenge. This paper proposes FedLECC (Federated Learning with Enhanced Cluster Choice), a lightweight, cluster-aware, and loss-guided client selection strategy for cross-device FL. FedLECC groups clients by label-distribution similarity and prioritizes clusters and clients with higher local loss, enabling the selection of a small yet informative and diverse set of clients. Experimental results under severe label skew show that FedLECC improves test accuracy by up to 12%, while reducing communication rounds by approximately 22% and overall communication overhead by up to 50% compared to strong baselines. These results demonstrate that informed client selection improves the efficiency and scalability of FL workloads in cloud-edge systems.
Daniel M. Jimenez-Gutierrez, Giovanni Giunta, Mehrdad Hassanzadeh +3
Sapienza University of Rome Via Ariosto 25 Rome, 00185, Italy