Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

Sound source localization (SSL) has been widely applied in industrial and civil fields. And with the development of wearable devices and the Internet of Things (IoT), it is attractive to deploy the SSL system onto embedded and portable devices. However, the software-based SSL system causes excessive response delay and is often affected by environmental noise. To overcome this obstacle, we propose the fast and precise localization (FPL) algorithm for distributed SSL systems. It combines the benefits of both time difference of arrival (TDOA) and steered response power (SRP) methods, and thus it is able to localize sound sources fast and precisely. To further improve the localization speed, we propose the fast and reconfigurable localization (FRL) accelerator, which is an algorithm-hardware co-designed SSL accelerator. It adopts multiple distributed localization nodes for higher localization precision and higher robustness to environmental interference, and it can be configured into either the fast or precise mode to adapt to various environments. Experimental evaluations show that our proposed FPL algorithm can achieve high localization speed and precision, and the field-programmable gate array (FPGA)-based FRL accelerator outperforms the software implementation by$48.6\times $and outperforms the prior FPGA-based SSL accelerators by$20\times \sim 838.2\times $.