Jul 2026· 2026 6th International Conference on Intelligent Communications and Computing (ICICC)· pp. 1-8· 0 citations· 17 references
Abstract
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.
Federated Learning (FL) enables collaborative model training without sharing raw data, but its effectiveness degrades under Non-IID client data and inefficient participation. In such settings, FedAvg may exhibit slow convergence, high variance across rounds, and elevated communication overhead. We propose a clustered ensemble framework to improve training stability and communication efficiency under label-skewed Non-IID distributions. Clients are clustered by data similarity, and one model is trained per cluster. At inference time, we employ a confidence-based ensemble that selects (or combines) cluster models, while adaptive client selection regulates participation within clusters to reduce redundant communication. Experiments on MNIST, Fashion-MNIST, CIFAR-10, and SVHN show more stable learning dynamics and lower communication cost while maintaining competitive accuracy compared to FedAvg. We also analyze how participation policies affect convergence behavior and client fairness.
Artur Sousa Freitas, A. T. Akabane, J. Estrella· International Conference on...· 0 citations
Conventional federated learning (FL) relies on parameter averaging, which forces clients to be doubly homogeneous: it demands an identical architecture and degrades under non-IID data. Real-world deployments usually break both assumptions. We sidestep both by building a decentralized knowledge distillation framework in which each client evaluates its peers'model snapshots on its own local data and distills from the resulting soft predictions. Because knowledge is transferred through the shared class posterior, clients are free to run different architectures; and because every teacher is evaluated on the student's own device, raw data never leaves the client, with no central server or public dataset required. Within this setting, we identify and address an under-examined problem: how to combine the peer teacher predictions. Existing methods, like uniform averaging, ignore how knowledge reliability varies across teachers and classes. We propose Class-wise Reliability-Aware Distillation (CRAD), which, per class, first discards teachers that disagree with the peer consensus and then takes a weighted average of the rest, weighting each teacher by its per-class reliability (precision, or inverse variance). Since the variance of an accuracy from $n$ samples scales as $1/n$, support enters automatically: among the teachers that survive filtering, a teacher is trusted for a class to the degree that it is both accurate and well-evidenced for it. On three image-classification benchmarks (CIFAR-10, CIFAR-100, and PathMNIST colon pathology), across heterogeneous architectures under severe non-IID skew, CRAD consistently outperforms competing methods in global accuracy.
Baraa Bilbeisi, Mengchen Fan, Baocheng Geng et al.· 0 citations
This work proposes a data-Quality-aware aggregation framework by introducing an Evolutionary-computation-inspired de-sign into Federated learning ( FedEvoQ), with a lightweight dual-branch architecture.
This paper proposes 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 that significantly improves accuracy and convergence under diverse non-IID settings.
Ruiqi Wu, Yehong Li, Hongjie Guo et al.· Computers, Materials & C...· 0 citations
Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space using an NTK-based agreement score to characterize predictive behavior and determine a personalized aggregation set for each client.
Mirko Konstantin, S. Zachow, Anirban Mukhopadhyay· 0 citations
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.
Jiahao Wu, Ming Hu, Yanxin Yang et al.· Advances in Neural Informati...· 1 citation
