Tactile sensing is essential for dexterous manipulation, yet large-scale human demonstration datasets lack tactile feedback, limiting their effectiveness in skill transfer to robots. To address this, we introduce TacCap, a wearable Fiber Bragg Grating (FBG)-based tactile sensor designed for seamless human-to-robot transfer. TacCap is lightweight, durable, and immune to electromagnetic interference, making it ideal for real-world data collection. We detail its design and fabrication, evaluate its sensitivity, repeatability, and cross-sensor consistency, and assess its effectiveness through grasp stability prediction and ablation studies. Our results demonstrate that TacCap enables transferable tactile data collection, bridging the gap between human demonstrations and robotic execution, with broad implications for fine-motor disciplines such as surgical training and musical performance. To support further research and development, we open-source our hardware design and software.

With the rapid development of the Internet of Things (IoT), IoT devices find applications in various domains. The data generated by these devices is utilized for analysis and services, especially in the field of Artificial Intelligence (AI) applied to IoT, known as Artificial Intelligence of Things (AIoT). The enhancement of edge device computing power in the IoT has led to the emergence of research areas like edge-cloud synergy AI theories and application services. In the context of lifelong learning and real-time processes in AIoT edge-cloud synergy services, addressing unseen tasks becomes crucial. Unseen tasks arise when inference requests from edge devices involve models not present in the cloud’s model repository. Addressing these challenges involves generating data to either augment small sample problems or alter the data distribution for heterogeneous sample issues. As the application of large language models (LLMs) for data generation gains traction, challenges emerge in the context of AIoT edge-cloud synergy services. Firstly, fine-tuning LLMs with heterogeneous data exacerbates model bias issues. Secondly, the substantial data requirements for training LLMs pose a contradiction. Lastly, the involvement of manual annotation in LLM-based data generation introduces complexity and cost. This paper proposes a framework Seafarer to these challenges using Generative Adversarial Networks and Self-taught Learning. Seafarer avoids model bias, reduces data requirements, and eliminates the need for manual annotation. The design demonstrates effectiveness theoretically and is validated on the Cityscapes dataset, achieving an 80% reduction in training loss and improved validation loss stability.

Federated unlearning (FU) has become an important area of research due to an increasing need for federated learning (FL) applications to comply with emerging data privacy regulations such as GDPR. It facilitates the removal of certain clients' data from an already trained FL model while preserving the performance on the remaining client without the need to retrain from scratch. Existing FU methods typically require clients to have access to their training data or historical model updates, which may be impractical in real-world scenarios due to privacy constraints and changes in data availability. Moreover, FU methods may cause catastrophic unlearning, where removing a client's data from heterogeneous, non-IID settings can negatively impact the model's performance on data from retained clients. To address the aforementioned issues and leverage the capabilities of personalized federated learning (pFL) in handling non-IID data distributions, this paper introduce Mimir, a novel data-free federated unlearning framework designed for pFL settings. Mimir integrates both learning and unlearning phases by utilizing personalized prompts for each client. We design a distillation structure based on Generative Adversarial Networks (GANs) for client-level unlearning that does not require access to original data or historical updates. By leveraging client-specific prompts generated during the pFL phase, Mimir adapts to heterogeneous data distributions and mitigates catastrophic unlearning on the retained data. We demonstrate the effectiveness of Mimir through extensive experiments on benchmark datasets, showing its ability to forget target client data while preserving model accuracy on the remaining clients.

We present FedSM, a Federated Spectrum Management architecture to increase channel utilization (CU) and reduce latency, while protecting users’ data privacy. We employ hedonic coalition formation game for spectrum allocation. Within each coalition, we design a bandit learning algorithm to share spectra and adjust resource usage. Preliminary simulation results show FedSM increases CU to 93.51% and reduces latency to 248.68 ms compared to three privacy-preserving dynamic spectrum management architectures.

Encrypted packet inspection (EPI) allows a middle-box to perform DPI over encrypted packets without decryption. Existing EPI systems rely on expensive cryptographic operations, hence they are not yet ready to be deployed in real-world. Fur-thermore, such solutions only support exact keyword matching, unable to securely support regular expression, which is the major tool for DPI rule description due to its powerful and flexible expressive ability. In this paper, we propose PrivRE, the first EPI system that can securely support regular expressions. The main idea of PrivRE is to have middlebox run regular expressions on a desensitized version of the payload, in which sensitive information has been replaced with dummy characters. We provide a full-fledged implementation of PrivRE. In particular, we override OpenSSL to make PrivRE transparent to the application layer, so that the software developers do not need to be aware of the existence of PrivRE. We systematically evaluate PrivRE on a testbed that consists of 3 intercontinental EC2 VMs. Our experimental results show that it introduces at most 0.03 % accuracy loss, and it is only 1.78 x −8.23 x slower than SplitTLS (where the middle box can decrypt the packets).

Generative Adversarial Networks (GANs) have become predominant in mobile computing for their ability to generate data. The concern for data privacy has made it arduous to collect large-scale datasets for GAN training on centralized servers. Federated Learning (FL) has emerged as a promising solution to address data privacy concerns. In this paper, we propose Oasis, a multiplayer-oriented federated GAN training system. We present a motivation, highlighting the Nash Equilibrium (NE) shift in vanilla federated GANs, exacerbated by data heterogeneity, leading to poor training performance with issues of vanishing gradient and mode collapse. To address mode collapse, Oasis extracts privacy-preserving data representations and generates a similarity table for clustering clients. Each group independently trains a GAN model and conducts distribution and fusion. By introducing a coordinator, Oasis generalizes intra-group games intoSeparable Zero-sum Multiplayer Gamesto tackle vanishing gradient. Thus, Oasis considers the overall federated GAN training asGroup-wise Separable Zero-sum Multiplayer Games. Practically, we evaluate our theoretical results both on a hardware prototype and in a simulated environment. Evaluation results demonstrate the effectiveness of Oasis, with an average improvement of 23.13% and 26.33% in terms of FID and NDB/K respectively, compared to threestate-of-the-artFL approaches over three datasets.