FedEvoQ: Evolutionary Diversity Probing for Data-Quality-Aware Federated Aggregation

EA-AQF: energy aware adaptive quantization and freezing in federated learning

Federated Learning (FL) enables collaborative model training across decentralized edge devices while preserving data privacy. However, real-world deployments are severely hindered by high uplink communication overhead and heterogeneous on-device computational constraints. To address these joint bottlenecks, We present Energy-Aware Adaptive Quantization and Freezing (EA-AQF), a unified framework that co-optimizes communication and computation. EA-AQF introduces a tiered mapping policy that assigns stage-wise activation bitwidths based on a dynamic client resource indicator. To further reduce local training costs, we implement a deterministic, precision-conditioned stage-freezing policy that disables gradient updates for computationally expensive network stages during backpropagation. Additionally, weight quantization is applied to client updates to minimize the communication footprint. We evaluate EA-AQF across diverse architectures, including CNN-7, CNN-17, and ResNet-20 on CIFAR-10, CIFAR-100, and MNIST datasets, and LSTM and MLP on IMDB and HAR datasets, under varying statistical distributions. Experimental results demonstrate that EA-AQF achieves a consistent communication reduction of 72.9% to 74.8% and significant computation reduction up to 12.66\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}, which is 1.6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} higher than prior state-of-the-art methods, with a marginal accuracy trade-off typically within 2-3% of full-precision baselines. Quantitatively, our sustainability analysis reveals that EA-AQF achieves up to 65% reduction in energy consumption compared to state-of-the-art, while strictly limiting carbon emissions to as low as 0.0122 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm {kgCO_2e}$$\end{document} for resource-intensive tasks. Notably, in highly heterogeneous tasks, EA-AQF maintains robust convergence positioning it as a highly efficient solution for sustainable edge AI.

Federated Learning with Consistency Optimization Algorithms under Non-IID Data

: Federated learning (FL) enables collaborative training of deep neural architectures while preserving data privacy, yet its performance often deteriorates in non-IID scenarios, which stems from client-side distribution drift and divergent local updates induced by pervasive data heterogeneity. This challenge is particularly critical for maintaining the structural consistency and generalization of neural models across diverse, distributed sources with significant distribution shifts. In this paper, we investigate how to effectively mitigate label distribution shift and feature distribution skew to enhance the global representation stability of neural architectures. We propose Federated Learning with Consistency Optimization Algorithms (FedCO), a novel optimization framework that incorporates a label-skew-aware correction loss and neural feature distribution regularization during local training. Specifically, our method aligns the internal statistics of architectural components between local and global models to suppress feature-space drift. Combined with an adaptive global aggregation mechanism guided by label entropy, this approach ensures that model updates from heterogeneous clients are consistently integrated into the global architectural parameters. Experimental results on multiple benchmarks demonstrate that FedCO significantly improves accuracy and convergence under diverse non-IID settings. For instance, on CIFAR-10 with extreme heterogeneity ( α = 0.05), FedCO achieves 73.64% accuracy, outperforming FedAvg by 7.32%; it also attains the highest accuracies on CIFAR-100 (62.63%) and TinyIma-geNet (37.82%) under the same setting. Our findings provide a robust strategy for integrating distribution-aware local training with adaptive structural aggregation, offering new insights into enhancing the reliability of distributed neural systems in real-world deployments. The code related to this algorithm is available at https://github.com/Donglin0730/ FedCO.

DASFL: Dynamic Adaptive Split Federated Learning for Heterogeneous Clients

IRW-FL: Improvement-Based Reliability Weighted Federated Learning

Federated Learning often suffers from significant performance degradation under Non-IID (non-identically and independently distributed) data settings, where heterogeneous client data distributions lead to severe model drift. Traditional aggregation strategies, such as FedAvg, typically determine client contribution weights based solely on local dataset sizes, neglecting the varying quality and relevance of individual client updates to the global model. In this paper, we propose a novel Improvementbased Reliability Weighted Federated Learning (IRW-FL) framework. The key idea is to estimate client reliability by measuring the net performance improvement achieved on a small local validation set after each round of local training. To reduce the impact of stochastic fluctuations across training rounds, we maintain an Exponential Moving Average (EMA) of these improvements to construct a stable long-term reliability estimate for each client. Following a brief warm-up phase, the reliability scores are normalized using a temperature-scaled Softmax function and combined with client dataset sizes to produce adaptive aggregation weights. In addition, we incorporate serverside momentum to further stabilize the global optimization process. Extensive experiments on the CIFAR-10 dataset using a lightweight CNN model under multiple Dirichlet-based Non-IID scenarios demonstrate that IRW-FL consistently outperforms strong baselines, including FedAvg and FedProx. We further conduct ablation studies on warm-up duration and temperature parameter sensitivity, and evaluate robustness under partial client participation. The proposed approach achieves higher global model accuracy and exhibits strong robustness under severe data heterogeneity.

FedHAttn: an attention-based homogeneous aggregation framework for energy-efficient and accurate PM2.5 prediction in IoAP-enabled edge devices

Rising from Ashes: Generalized Federated Learning via Dynamic Parameter Reset

A novel FL framework is presented, FedPhoenix, that stochastically re-sets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific overfitting features.