The research on Wi-Fi sensing has been thriving over the past decade but the process has not been smooth. Three barriers always hamper the research: 1) unknown baseband design and its influence; 2) inadequate hardware; and 3) the lack of versatile and flexible measurement software. This article tries to eliminate these barriers through the following work. First , we present an in-depth study of the baseband design of the Qualcomm Atheros AR9300 (QCA9300) NIC. We identify a missing item of the existing channel state information (CSI) model, namely, the CSI distortion, and identify the baseband filter as its origin. We also propose a distortion removal method. Second , we reintroduce both the QCA9300 and software-defined radio (SDR) as powerful hardware for research. For the QCA9300, we unlock the arbitrary tuning of both the carrier frequency and bandwidth. For SDR, we develop a high-performance software implementation of the 802.11a/g/n/ac/ax baseband, allowing users to fully control the baseband and access the complete physical-layer information. Third , we release the PicoScenes software, which supports concurrent CSI measurement from multiple QCA9300, Intel Wireless Link (IWL5300), and SDR hardware. PicoScenes features rich low-level controls, packet injection, and software baseband implementation. It also allows users to develop their own measurement plugins. Finally , we report state-of-the-art results in the extensive evaluations of the PicoScenes system, such as the >2-GHz available spectrum on the QCA9300, concurrent CSI measurement, and up to 40 and 1 kHz CSI measurement rates achieved by the QCA9300 and SDR. PicoScenes is available at https://ps.zpj.io .

Federated learning (FL) is an emerging distributed and privacy-preserving machine learning framework. However, the performance of traditional FL methods is seriously impaired by the real-world data, which appear to be non-IID. The recent clustered federated learning (CFL) methods eliminate the impact of non-IID data by grouping clients with similar data distribution into the same cluster. Unfortunately, existing CFL methods heavily rely on the pre-setting of the cluster number, failing to achieve adaptive client clustering. We also experimentally observe that imbalanced data largely degrade their correctness of client clustering. In this paper, we present a novel CFL method without manual intervention, named AutoCFL, which can eliminate both effects of non-IID and imbalanced data simultaneously. To deal with imbalanced data, the local training adjustment strategy adaptively adjusts the number of local training epochs for each client. To further improve the clustering correctness and adaptability, the weighted voting-based client clustering strategy automatically groups each client into an appropriate cluster. Extensive experiments are conducted to evaluate the design of AutoCFL with three popular datasets under various data settings. Experimental results demonstrate that AutoCFL outperforms state-of-the-art methods, e.g., on average improving model accuracy by 9.24%, while reducing communication costs by 4.67 in an adaptive manner.

Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This combination allows the large foundation models and the small local domain models at the remote clients to learn from each other in a teacher-student learning setting. This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency. The ten challenging problems manifest in five pivotal aspects: “Foundational Theory,” which aims to establish a coherent and unifying theoretical framework for FedFMs. “Data,” addressing the difficulties in leveraging domain-specific knowledge from private data while maintaining privacy; “Heterogeneity,” examining variations in data, model, and computational resources across clients; “Security and Privacy,” focusing on defenses against malicious attacks and model theft; and “Efficiency,” highlighting the need for improvements in training, communication, and parameter efficiency. For each problem, we offer a clear mathematical definition on the objective function, analyze existing methods, and discuss the key challenges and potential solutions. This in-depth exploration aims to advance the theoretical foundations of FedFMs, guide practical implementations, and inspire future research to overcome these obstacles, thereby enabling the robust, efficient, and privacy-preserving FedFMs in various real-world applications.

Recent defense approaches against targeted model poisoning attacks aim to prevent specific prediction failures in federated learning (FL). However, these defenses remain susceptible to targeted collusion attacks, particularly under conditions of high proportions of malicious clients and attack density. To address these vulnerabilities, we propose RoseAgg, which dynamically identifies a plausible clean ingredient from local updates and leverages it to constrain the influence of poisoned updates. Firstly, RoseAgg recognizes and confines common characteristics found in poisoned updates, such as scaled-up magnitudes or similar directional contributions. Furthermore, RoseAgg dynamically extracts a plausible clean ingredient using a dimension-reduction method. This clean ingredient becomes the foundation for the server to bootstrap credit scores for each local update, ensuring the dominance of benign updates over poisoned ones. Ultimately, the server computes a weighted average of local updates based on credit scores, generating a global update for refining the global model. Comprehensive evaluations on four benchmark datasets showcase RoseAgg’s effectiveness against seven advanced attacks. The code is available at https://github.com/SleepedCat/RoseAgg.

Clustered federated learning (CFL) has proved to be an effective way to alleviate the non-IID (not independently and identically distributed) data challenge, which severely restricts the wider application of federated learning. However, existing approaches either lack adaptability, i.e. , they require an additional number of clusters as a guide when clustering, or lack effectiveness in terms of communication. In this paper, we explore the differences in the ability of different layers in a model to represent non-IID data, and propose a hierarchical CFL approach, named HiCFL , which considers both adaptivity and communication efficiency. The improvement of communication efficiency is due to our proposed novel concept of model stability, which characterizes the variation of model weights during training. Based on model stability, HiCFL can find the proper time to bi-partition the clusters of mobile devices in a hierarchical manner more quickly. We conduct extensive experiments based on popular datasets with various non-IID data settings. The results show that HiCFL achieves excellent performance effectiveness and efficiency. Compared to state-of-the-art approaches, HiCFL can improve the model accuracy by $2.0\% \sim 9.0\%$ , while reducing the communication overheads by $27.3\% \sim 80.6\%$ .