220 GHz RIS-Aided Multi-user Terahertz Communication System: Prototype Design and Over-the-Air Experimental Trials

Terahertz (THz) communication technology is regarded as a promising enabler for achieving ultra-high data rate transmission in next-generation communication systems. To mitigate the high path loss in THz systems, the transmitting beams are typically narrow and highly directional, which makes it difficult for a single beam to serve multiple users simultaneously. To address this challenge, reconfigurable intelligent surfaces (RIS), which can dynamically manipulate the wireless propagation environment, have been integrated into THz communication systems to extend coverage. Existing works mostly remain theoretical analysis and simulation, while prototype validation of RIS-assisted THz communication systems is scarce. In this paper, we designed a liquid crystal-based RIS operating at 220 GHz supporting both single-user and multi-user communication scenarios, followed by a RIS-aided THz communication system prototype. To enhance the system performance, we developed a beamforming method including a real-time power feedback control, which is compatible with both single-beam and multibeam modes. To support simultaneous multi-user transmission, we designed an OFDM-based resource allocation scheme. In our experiments, the received power gain with RIS is no less than 10 dB in the single-beam mode, and no less than 5 dB in the multi-beam mode. With the assistance of RIS, the achievable rate of the system could reach 2.341 Gbps with 3 users sharing 400 MHz bandwidth and the bit error rate (BER) of the system decreased sharply. Finally, an image transmission experiment was conducted to vividly show that the receiver could recover the transmitted information correctly with the help of RIS. The experimental results also demonstrated that the received signal quality was enhanced through power feedback adjustments.

On Performance of LoRa Fluid Antenna Systems

This paper advocates a fluid antenna system (FAS) assisting long-range communication (LoRa-FAS) for Internet-of-Things (IoT) applications. Our focus is on pilot sequence overhead and placement for FAS. Specifically, we consider embedding pilot sequences within symbols to reduce the equivalent symbol error rate (SER), leveraging the fact that the pilot sequences do not convey source information and correlation detection at the LoRa receiver needs not be performed across the entire symbol. We obtain closed-form approximations for the probability density function (PDF) and cumulative distribution function (CDF) of the FAS channel, assuming perfect channel state information (CSI). Moreover, the approximate SER, hence the bit error rate (BER), of the proposed LoRa-FAS is derived. Simulation results indicate that substantial SER gains can be achieved by FAS within the LoRa framework, even with a limited size of FAS. Furthermore, our analytical results align well with that of the Clarke's exact spatial correlation model. Finally, the correlation factor for the block correlation model should be selected as the proportion of the exact correlation matrix's eigenvalues greater than $1$.

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs with Minimal Human Interventions

Recent AI agents, such as ChatGPT and LLaMA, primarily rely on instruction tuning and reinforcement learning to calibrate the output of large language models (LLMs) with human intentions, ensuring the outputs are harmless and helpful. Existing methods heavily depend on the manual annotation of high-quality positive samples, while contending with issues such as noisy labels and minimal distinctions between preferred and dispreferred response data. However, readily available toxic samples with clear safety distinctions are often filtered out, removing valuable negative references that could aid LLMs in safety alignment. In response, we propose PT-ALIGN, a novel safety self-alignment approach that minimizes human supervision by automatically refining positive and toxic samples and performing fine-grained dual instruction tuning. Positive samples are harmless responses, while toxic samples deliberately contain extremely harmful content, serving as a new supervisory signals. Specifically, we utilize LLM itself to iteratively generate and refine training instances by only exploring fewer than 50 human annotations. We then employ two losses, i.e., maximum likelihood estimation (MLE) and fine-grained unlikelihood training (UT), to jointly learn to enhance the LLM's safety. The MLE loss encourages an LLM to maximize the generation of harmless content based on positive samples. Conversely, the fine-grained UT loss guides the LLM to minimize the output of harmful words based on negative samples at the token-level, thereby guiding the model to decouple safety from effectiveness, directing it toward safer fine-tuning objectives, and increasing the likelihood of generating helpful and reliable content. Experiments on 9 popular open-source LLMs demonstrate the effectiveness of our PT-ALIGN for safety alignment, while maintaining comparable levels of helpfulness and usefulness.

FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection

Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping local gradient transmission with continuous local computing, FL can remarkably reduce its training latency over homogeneous clients, yet encounter severe model staleness, model drifts, memory cost and straggler issues in heterogeneous environments. To unleash the full potential of overlapping, we propose, FedEx, a novel \underline{fed}erated learning approach to \underline{ex}pedite FL training over mobile devices under data, computing and wireless heterogeneity. FedEx redefines the overlapping procedure with staleness ceilings to constrain memory consumption and make overlapping compatible with participation selection (PS) designs. Then, FedEx characterizes the PS utility function by considering the latency reduced by overlapping, and provides a holistic PS solution to address the straggler issue. FedEx also introduces a simple but effective metric to trigger overlapping, in order to avoid model drifts. Experimental results show that compared with its peer designs, FedEx demonstrates substantial reductions in FL training latency over heterogeneous mobile devices with limited memory cost.

WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling

As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing and communications capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, it is unaware of (i) the dynamic changes of devices' communication and computing conditions and (ii) FL training progress and its dynamic requirements of local training contributions, both of which may cause very long FL training delay. Motivated by those dynamics, in this paper, we develop a wireless and heterogeneity aware latency efficient FL (WHALE-FL) approach to accelerate FL training through adaptive subnetwork scheduling. Instead of sticking to the fixed size subnetwork, WHALE-FL introduces a novel subnetwork selection utility function to capture device and FL training dynamics, and guides the mobile device to adaptively select the subnetwork size for local training based on (a) its computing and communication capacity, (b) its dynamic computing and/or communication conditions, and (c) FL training status and its corresponding requirements for local training contributions. Our evaluation shows that, compared with peer designs, WHALE-FL effectively accelerates FL training without sacrificing learning accuracy.

Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling

Federated Learning (FL) algorithms commonly sample a random subset of clients to address the straggler issue and improve communication efficiency. While recent works have proposed various client sampling methods, they have limitations in joint system and data heterogeneity design, which may not align with practical heterogeneous wireless networks. In this work, we advocate a new independent client sampling strategy to minimize the wall-clock training time of FL, while considering data heterogeneity and system heterogeneity in both communication and computation. We first derive a new convergence bound for non-convex loss functions with independent client sampling and then propose an adaptive bandwidth allocation scheme. Furthermore, we propose an efficient independent client sampling algorithm based on the upper bounds on the convergence rounds and the expected per-round training time, to minimize the wall-clock time of FL, while considering both the data and system heterogeneity. Experimental results under practical wireless network settings with real-world prototype demonstrate that the proposed independent sampling scheme substantially outperforms the current best sampling schemes under various training models and datasets.