FedAnchor is proposed, an anchored and adaptive FL framework for resource-heterogeneous fault diagnosis that couples resource-aware submodel allocation with structurally aligned aggregation and achieves competitive or superior accuracy compared with representative homogeneous and model-heterogeneous FL baselines.
Abstract
Federated learning (FL) enables privacy-preserving fault diagnosis across distributed industrial devices, but most existing methods assume homogeneous model architectures and comparable client resources. This assumption is unrealistic in resource-constrained Industrial Internet of Things (IIoT) scenarios, where clients may have substantially different memory and computation capacities. To address this challenge, we propose FedAnchor, an anchored and adaptive FL framework for resource-heterogeneous fault diagnosis. FedAnchor decomposes each client submodel into a shared anchored core and a client-specific adaptive extension. The anchored core provides a common parameter subspace for consistent masked aggregation, while the adaptive extension is selected by a server-side reinforcement-guided policy under client memory budgets. This design couples resource-aware submodel allocation with structurally aligned aggregation. Experiments on four benchmark datasets and six heterogeneous memory configurations show that FedAnchor achieves competitive or superior accuracy compared with representative homogeneous and model-heterogeneous FL baselines. Under the evaluated non-IID settings, FedAnchor improves accuracy by up to 9.8 percentage points over the strongest baseline, while maintaining favorable communication–accuracy trade-offs and empirical stability.
Timely and accurate defect classification is critical for ensuring product quality and safety in industrial inspection scenarios. The widespread deployment of Internet of Things (IoT) devices equipped with sensing, computing, and communication capabilities has promoted the development of AI-enabled industrial applications. However, conventional AI approaches typically rely on centralized data collection and processing, which become impractical in real-world IoT environments due to growing privacy concerns and constrained device resources. To address these challenges, this paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks. The proposed approach jointly accelerates the training process through three mechanisms: (i) adaptive local updates that balance communication and computation overheads; (ii) parameter compression that trades off communication cost against model accuracy; (iii) joint bandwidth and computation-power allocation that optimizes per-round communication and computation time across participating devices. We further analyze the joint effects of these three mechanisms and provide a convergence analysis. Extensive simulations show that the proposed method achieves competitive classification accuracy while reducing single-round training time by up to 70%.
Shuo He, He-Yang Wei, Congxian Bi et al.· Electronics· 0 citations
Experiments show that LBSFL achieves competitive model accuracy while substantially reducing computational and communication overhead in most evaluated settings, indicating that LBSFL provides a favorable trade-off between robustness and efficiency for IoT-oriented federated learning.
Wei Ma, Wenjun Tian, Qihang Zhao et al.· Journal of King Saud Univers...· 0 citations
Federated learning (FL) on heterogeneous edge networks faces a fundamental tension: standard aggregation protocols assume client homogeneity, yet real-world edge deployments span device tiers with $7 \times$ compute and $\mathbf{1 0} \times$ bandwidth disparities. Slow clients become stragglers that stall synchronous rounds, while uniform gradient compression degrades accuracy on bandwidth-constrained devices. This paper presents FedEdge-Adapt, a novel adaptive federated learning framework that jointly addresses device heterogeneity, straggler mitigation, and communication efficiency without sacrificing model quality. FedEdge-Adapt introduces three tightly coupled mechanisms: (1) tier-aware gradient compression that applies device-class-specific sparsification ratios, (2) drift-corrected aggregation that reweights client updates based on staleness and data heterogeneity, and (3) predictive client selection that anticipates dropout-prone devices using a lightweight resource oracle. We evaluate FedEdge-Adapt on a 30-node heterogeneous edge network over 150 communication rounds using the CIFAR-10 dataset under non-IID distributions ($\alpha=0.5$ Dirichlet) and compare against FedAvg, FedProx, and SCAFFOLD baselines. FedEdge-Adapt achieves 85.44% global accuracy, a 6.85 percentage-point improvement over FedAvg, while simultaneously reducing round latency by $\mathbf{6 7. 4 \%}$, communication overhead by $\mathbf{3 4. 0 \%}$, and client dropout rate by $\mathbf{5 4. 0 \%}$. Convergence is reached in 18 rounds versus 31 for FedAvg. Extended experiments across 100+ rounds confirm long-term stability with no late-stage divergence.
Saher Elsayed, Mohamed Ali, Samer Abubaker et al.· Annual International Compute...· 0 citations
The Federated Green Anaconda Optimizer (FedGAO), an innovative FL framework inspired by the behavioral patterns of the Green Anaconda Optimizer (GAO), is proposed, demonstrating superior performance in terms of accuracy, convergence speed, and resource efficiency.
Elahe Eslami, S. A. Shahzadeh Fazeli, J. Abouei et al.· Cluster Computing· 0 citations
Prototype-based knowledge sharing effectively mitigates data and model heterogeneity in federated learning (FL) by exchanging class-level semantic information. However, existing methods typically assume all local prototypes are equally reliable. Consequently, low-quality prototypes from heterogeneous models or dynamic clients can contaminate the global aggregation, leading to a vicious cycle of noise accumulation and performance degradation. To address this, we propose FedLEAF, a Federated Learning framework with server-side proactive Evaluation and clientside Adaptive Fusion. Specifically, the server employs an Adaptive Learning Prototypes (ALP) network to dynamically evaluate prototype reliability and generate learnable aggregation weights, ensuring that highquality prototypes exert a primary influence on the global model. Meanwhile, the client utilizes a Historical Consistency Fusion (HCF) strategy to selectively absorb global knowledge by assessing its consistency with locally maintained historical prototypes. Extensive experiments on standard datasets demonstrate that FedLEAF achieves effective improvements in model accuracy and robustness compared to existing methods.
Zhiyuan Zhu, Si-Yi Deng, Dapeng Wu et al.· 2026 International Conferenc...· 0 citations
Federated learning (FL) collaboratively trains models across networked industrial Internet of Things (IIoT) terminals. However, statistical heterogeneity in IIoT data often hinders the performance of global models. Current FL methods typically focus on single-level representation alignment and fail to exploit gradient-guided semantic feedback across hierarchical layers. To address statistical heterogeneity, we propose federated hierarchical prototype learning (FedHiPL), which models local representations with Gaussian prototypes and improves global learning through hierarchical prototype calibration. First, FedHiPL performs multilevel prototype alignment based on symmetric Kullback–Leibler divergence to enforce representation consistency across layers. Second, FedHiPL calibrates the local decision head by balancing local and global decision objectives with decision consistency constraints. Third, FedHiPL rectifies global prototypes through a gradient-guided hierarchical calibration module to maintain structural consistency across network layers. Experiments on a custom-constructed distributed cluster demonstrate that FedHiPL achieves 93.24% accuracy on Edge-IIoT and 72.36% accuracy on UNSW-NB15 under strong statistical heterogeneity, outperforming the representative prototype-based baseline FedProto by 6.06% and 15.80%, respectively.
Fang Li, Zhi-Cheng Chen, Jinghao Liu et al.· IEEE Internet of Things Jour...· 0 citations
