Personalized federated learning (PFL) tailors models to clients' unique data distributions while preserving privacy. However, existing aggregation-weight-based PFL methods often struggle with heterogeneous data, facing challenges in accuracy, computational efficiency, and communication overhead. We propose FedAPA, a novel PFL method featuring a server-side, gradient-based adaptive aggregation strategy to generate personalized models, by updating aggregation weights based on gradients of client-parameter changes with respect to the aggregation weights in a centralized manner. FedAPA guarantees theoretical convergence and achieves superior accuracy and computational efficiency compared to 10 PFL competitors across three datasets, with competitive communication overhead. The code and full proofs are available at: https://github.com/Yuxia-Sun/FL_FedAPA.

The escalating prevalence of Advanced Persistent Threat (APT) malware demands more effective methods to accurately attribute malware to specific APT groups. Traditional manual attribution processes are labor-intensive and error-prone, while existing automated methods are hampered by small dataset sizes, inadequate representation learning, and poor noise reduction during preprocessing. To address these challenges, we introduce the AMG25 dataset, which expands the pool of malware samples labeled with APT group affiliations. Concurrently, we propose the MGAP3model (Malware Group Attribution based on PerceiverIO and Polytype Pre-training), which enhances attribution performance by incorporating hierarchical pre-training for disassembled codes and leveraging multi-view statistical features, all within a unified PerceiverIO architecture. This model adeptly captures complex program structures and interactions cross multiple code granularities, through a series of innovative polytype pre-training tasks. Additionally, we have developed a novel noise filtering technique that focuses on user-defined function codes, substantially reducing overfitting and boosting performance. Furthermore, a streamlined version of the model, MGAP3-Lite, has been developed to accelerate training while preserving robust performance. Extensive experiments have validated the effectiveness of our models and underscored the importance of the proposed pre-training technique.

Mobile Edge Computing (MEC) facilitates computing and storage at edge nodes near user devices, reducing latency and optimizing bandwidth. Federated Learning (FL) complements MEC by enabling privacy-preserving collaborative model training across edge nodes without sharing raw data. However, in MEC environments, FL faces challenges such as communication inefficiency and data heterogeneity (Non-IID), which degrade model performance and hinder convergence. To address these issues, we propose FedLFP, a communication-efficient personalized federated learning approach using label-free prototypes for Non-IID data in MEC. FedLFP employs three key strategies: (1) a Label-Free Prototype strategy to reduce communication costs and mitigate privacy risks, (2) a centroid prototype and combined clustering weight strategy to improve global prototype quality by considering data quantity and confidence levels, and (3) a multifaceted weighted contrastive learning strategy to enhance local representation learning and global alignment. We evaluated FedLFP on Android malware recognition using the KronoDroid dataset and standard image classification tasks, with eight configurations representing practical Non-IID settings. Experimental results show that FedLFP consistently outperforms thirteen state-of-the-art FL methods in accuracy, communication and computational efficiency. Additionally, we provide theoretical guarantees for the convergence of FedLFP under Non-IID conditions.