Federated learning has emerged as a promising paradigm for collaborative model training while preserving data privacy. However, many existing FL methods implicitly assume that clients have sufficient computational and storage resources, making them less applicable in real-world scenarios with severe system heterogeneity. To address this, submodel extraction has recently gained attention as a promising strategy to tailor the global model to resource-constrained clients. Despite this progress, existing methods often suffer from noticeable performance gaps across clients and structural inconsistency in the extracted models, leading to degraded global performance and increased communication overhead. In this work, we propose FedLAGC, a novel federated framework that jointly tackles performance imbalance and communication inefficiency through Layer-Adaptive submodel extraction and Gradient Correction. Specifically, FedLAGC constructs client-specific submodels by selecting structurally important parameters according to layer-wise importance scores, ensuring both resource adaptiveness and architectural consistency. Additionally, we propose a lightweight correction mechanism that captures historical optimization drift, helping to align local updates with the global direction and reduce redundant communication. The rigorous convergence analysis of FedLAGC for system-heterogeneous federated learning under non-convex objectives is given. Extensive experiments on CIFAR-10 and CIFAR-100 with ResNet-18 and ResNet-34 under various system and data heterogeneity settings demonstrate the significant superiority of FedLAGC (up to 24% accuracy improvement and 3.66× communication efficiency) over state-of-the-art methods.

Recently, the success of large models has demonstrated the importance of scaling up model sizes. However, it is difficult to directly train large models locally on multiple mobile devices due to their intrinsic computational constraints. To address this challenge, it becomes a crucial need to train larger global models by training small local models on devices. As a distributed learning approach, federated learning (FL) allows multiple devices to train models locally and aggregate them to form the global model by sharing the updated parameters with the server, thus enabling the co-training of models. This promising feature has spurred an increasing interest in exploring the collaborative training of large models. Despite the advent of existing device-heterogeneity FL approaches, they still have limitations in fully covering the parameter space of the global model. To fill this gap, we propose a novel approach calledFedBRB(Block- wiseRolling and weightedBroadcast). The core idea of FedBRB is to utilize local models of small devices to train all modules of a large global model and broadcast the trained parameters to the entire space, thereby enabling faster information sharing. This approach not only improves training efficiency but also fully utilizes limited computational resources. Experiments demonstrate that FedBRB can produce significant performance gains, achieving state-of-the-art results. Additionally, this paper provides theoretical and experimental analyses of FedBRB convergence, thereby paving a theoretical ground and providing practical guidance for further research and application of the FedBRB method.

In this article, we propose an efficient Takagi–Sugeno fuzzy zeroing neural network (TS-FZNN) activated by a new activation function for solving the time-varying Sylvester equation. The self-adaptive convergence parameter is designed by the Takagi–Sugeno fuzzy logic system. Convergence and robustness of the proposed model are analyzed. Theoretical analysis shows that the proposed model not only has less fixed convergence time than the recently suggested ZNN models, but also is noise-tolerant. Numerical experiments are performed to illustrate its efficiency and effectiveness as well as the superior performance over the existing ZNN models for solving the time-varying Sylvester equation, including the applicability of the proposed model to robot manipulator. The effects of model parameters on the dynamic response of the robotic manipulator system are also illustrated by the convergence rate of position errors of the robotic manipulator trajectory.