In federated learning (FL), the assumption that datasets from different devices are independent and identically distributed (i.i.d.) often does not hold due to user differences, and the presence of various data modalities across clients makes using a single model impractical. Personalizing certain parts of the model can effectively address these issues by allowing those parts to differ across clients, while the remaining parts serve as a shared model. However, we found that partial model personalization may exacerbate client drift (each client's local model diverges from the shared model), thereby reducing the effectiveness and efficiency of FL algorithms. We propose an FL framework based on the alternating direction method of multipliers (ADMM), referred to as FedAPM, to mitigate client drift. We construct the augmented Lagrangian function by incorporating first-order and second-order proximal terms into the objective, with the second-order term providing fixed correction and the first-order term offering compensatory correction between the local and shared models. Our analysis demonstrates that FedAPM, by using explicit estimates of the Lagrange multiplier, is more stable and efficient in terms of convergence compared to other FL frameworks. We establish the global convergence of FedAPM training from arbitrary initial points to a stationary point, achieving three types of rates: constant, linear, and sublinear, under mild assumptions. We conduct experiments using four heterogeneous and multimodal datasets with different metrics to validate the performance of FedAPM. Specifically, FedAPM achieves faster and more accurate convergence, outperforming the SOTA methods with average improvements of 12.3% in test accuracy, 16.4% in F1 score, and 18.0% in AUC while requiring fewer communication rounds.

In this paper, we introduce DLAPID, a novel decoupled parallel hardware-software co-design architecture for real-time video dehazing. From a software point of view, DLAPID isolates the atmospheric light operation from the initial transmission estimation to take full advantage of the hardware accelerators' parallelization features. For the hardware implementation, we deploy DLAPID both on FPGA and GPU platforms and validate its effectiveness. Using both real-world driving scenario testing sets and ground-truth datasets, we quantitatively and qualitatively assess the proposed method against several SOTA (state-of-the-art) video dehazing models. The outcomes of our experiments demonstrate that our approach achieves better dehazing performance with lower power consumption and has real-time processing capabilities, thereby preventing potential accidents of autonomous vehicles.