Heju Li

Federated learning (FL) enables collaborative model training across multiple Internet of Things (IoT) devices while preserving data privacy. However, the trustworthiness of the aggregation server (AS) remains a critical security vulnerability, especially in IoT environments that rely on potentially untrusted servers. Existing secure aggregation schemes exhibit critical flaws: verification mechanisms and aggregation processes are decoupled, allowing a malicious server to generate valid verification tokens while returning incorrect aggregation results. This enables covert attacks where verification succeeds despite erroneous models, thereby seriously compromising system reliability. As a result, designing a privacy-preserving aggregation scheme that integrates verification with aggregation, while keeping both low computational and communication costs, remains a persistent challenge. To address this issue, we propose a lightweight verifiable secure aggregation based on functional encryption for federated learning on IoT devices (VFE). Our protocol cryptographically binds multiclient function encryption to identity-based aggregate signatures, thereby deeply integrating aggregation computation with verification. We further employ an efficient bilinear-pairing-based verification protocol that supports single-step verification, thereby eliminating auxiliary mechanisms and significantly reducing verification complexity. The security analysis and extensive testing demonstrate that the proposed VFE protocol achieves robust verifiable aggregation in FL, while simultaneously preserving client privacy and substantially reducing both computation and communication overhead, making it highly suitable for IoT deployments.

Federated edge learning (FEEL) is an innovative approach that facilitates collaborative training among numerous distributed edge devices while eliminating the need to transfer sensitive information. However, the practical deployment of FEEL faces significant constraints, owing to the limited and asymmetric computational and communication resources of these devices, along with their energy availability. To this end, we propose a novel asymmetry-tolerant training approach for FEEL, enabled via simultaneous wireless information and power transfer (SWIPT). This framework leverages SWIPT to offer sustainable energy support for devices while enabling them to train models with varying intensities. Given a limited energy budget, we highlight the critical trade-off between heterogeneous local training intensities and the quality of wireless transmission, suggesting that the design of local training and wireless transmission should be closely integrated, rather than treated as separate entities. To elucidate this perspective, we rigorously derive a new explicit upper bound that captures the combined impact of local training accuracy and the mean square error of wireless aggregation on the convergence performance of FEEL. To maximize overall system performance, we formulate two key optimization problems: the first aims to maximize the energy harvesting capability among all devices, while the second addresses the joint learning–communication optimization under the optimal energy harvesting solution. Comprehensive experiments demonstrate that our proposed framework achieves significant performance improvements compared to existing baselines.

As a prominent and appealing paradigm, federated edge learning (FEEL) aims to sidestep sensitive information transfer by orchestrating collaborative training across a multitude of distributed edge devices. This approach overcomes the shortcomings of centralized machine learning, particularly in the realms of resource constraints, latency requirements, and privacy concerns. Within current FEEL algorithms, the alternating direction method of multipliers (ADMM) stands out as an emerging enabling technique, which has been confirmed to be communication-efficient and exhibits linear convergence under very mild conditions. Nevertheless, the significant premise to effectively implement the ADMM-based FEEL system in most of the prior works is the assumption of ideal parameter exchange between edge devices and the parameter server, which is extremely unrealistic arising from the unfavorable propagation deviation caused by wireless channels. To this end, we focus in this paper on analyzing the training performance of the ADMM-based FEEL system in the presence of noisy wireless channels, and accordingly deploy the reconfigurable intelligent surface (RIS) to simultaneously assist both the uplink and the downlink transmission. Specifically, we innovatively present an explicit convergence upper bound of the training performance of the ADMM-based FEEL system, and subsequently, a joint resource optimization problem with a constraint of maximum communication delay is further established to optimize the uplink transmission design, the shared RIS phase configuration, and the downlink broadcast beamforming. Simulation results validate a significant performance enhancement achieved with the proposed approach compared to existing solutions.

As a prominent and appealing paradigm, federated edge learning (FEEL) aims to orchestrate collaborative training across a multitude of distributed edge devices without the need for sensitive information transfer. However, device heterogeneity and wireless transmission distortion, which compromise training robustness and efficiency, severely hinder FEEL deployment in real-world applications. To this end, we propose in this article a novel FEEL framework empowered by simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with flexible aggregation. This framework harmonizes devices to train models with heterogeneous intensity in each communication round, while the deployment of STAR-RIS significantly boosts the quality of wireless aggregation. Here, we emphasize that there may exist a crucial tradeoff between the heterogeneous local training intensity and the transmission quality, particularly when edge devices operate under a limited energy budget. To illuminate this perspective, we rigorously derive a novel explicit upper bound that captures the joint impact of local training accuracy and the mean square error of wireless aggregation on FEEL convergence performance. Our theoretical results indicate that a naive focus on improving the local training accuracy within a constrained energy budget may ultimately detract from overall training performance. This finding sharply contrasts with existing research, which typically aims to accelerate convergence by increasing local training intensity while neglecting the impact of wireless aggregation distortion. To strike the ideal balance, we formulate a mixed-integer nonlinear programming problem to guide the joint design of the beamforming at devices and the base station, the configuration mode of STAR-RIS, and the local training intensity. Comprehensive experiments on representative datasets demonstrate that our proposed framework achieves significant performance improvements compared with existing baselines.

As an emerging distributed learning framework, federated edge learning (FEEL) can efficaciously resolve the delay requirements and privacy concerns by enabling collaborative modeling among the edge devices under the premise of data localization. However, the communication bottlenecks, e.g., model damage and signal deviation, will critically diminish the convergence performance due to the restricted resources and the undesirable wireless fading. To overcome this challenge, one feasible way is to integrate the reconfigurable intelligent surface (RIS) into the FEEL system, to reinforce the communication quality by adaptively reconfiguring the signal propagation environment. However, the significant premise to effectively exploit the RIS in most of the prior works is the estimation of exact instantaneous channel state information (CSI), which is extremely thorny and potentially incurs additional communication overhead. To tackle this issue, we investigate in this paper the RIS-aided FEEL system under the realistic supposition where only the statistical CSI is available among devices. Specifically, considering the wireless outage caused by the uncertainty of non-line-of-sight components, we rigorously derive an explicit convergence upper bound of the RIS enabled FEEL framework with outage. Accordingly, a resource configuration problem with the goal of minimizing the sum of outage-probability is further formulated by jointly configuring the RIS configuration matrix and the bandwidth allocation. To seek the solutions, we carefully design a general Bernstein-Type inequality in this paper, and thus the probabilistic outage objective function can be effectively handled in an equivalent manner. Simulation experiments verify that our design can accomplish a significant promotion compared against state-of-the-art baselines.

Federated edge learning (FEEL) as an emerging distributed learning paradigm can effectively resolve the resource constraints and privacy issues in the Internet of Things (IoT) by featuring collaborative training at the edge devices under the premise of data localization. Nevertheless, owing to the scarce resources and the inscrutable communication fading, issues such as model damage and signal deviation will critically diminish the convergence performance and learning accuracy of FEEL. With this in mind, reconfigurable intelligent surface (RIS) has recently been integrated to enhance the communication quality of wireless systems by adaptively reconfiguring the signal propagation environment. To utterly release the applied potency of RIS, the configuration of phase shifts is of paramount importance, where accurate channel state information (CSI) is required. Unfortunately, the accurate instantaneous CSI is extremely challenging to be estimated. In this paper, we focus on the realistic scenario assuming only statistical CSI is known, which can be comparatively easily and accurately explored. On the other hand, considering the wireless outage caused by the random non-line-of-sight channel, we rigorously derive an explicit convergence upper bound of the RIS enabled FEEL framework with respect to the outage probability. Based on the convergence theory, a unified resource allocation problem is further established by jointly optimizing bandwidth allocation, and the RIS configuration matrix. Extensive simulations are conducted to demonstrate that the proposed design dramatically promotes the learning performance compared against the baseline solutions.

In this paper, the problem of model aggregation for the federated edge learning (FEEL) over a realistic wireless network is investigated, where multiple distributed edge devices collaboratively train a global learning model using local data. In the considered model, the one-bit gradient quantization with majority vote is adopted at edge devices, which sends the local gradient sign to the edge server instead of transmitting high-dimensional stochastic gradients directly. After aggregating the quantified signs, the edge server sends back only the majority decision to significantly minimize the transmission overhead since all communications are compressed to one bit. Nevertheless, it turns out that the quality of training will be inevitably deteriorated by the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a reconfigurable intelligent surface (RIS) assisted one-bit communication scheme under orthogonal frequency division multiplexing (OFDM) system to reduce the signal distortion during iterative model exchange of FEEL. Specifically, a learning convergence analysis with respect to wireless communication error is first established. After that, we further formulate a unified communication-learning design problem to jointly optimize the power allocation vector and the RIS configuration matrix. Numerical results demonstrate that the proposed design achieves substantial performance improvement compared with the state-of-the-art solutions.

As one of the most popular and attractive frameworks for model training, federated edge learning (FEEL) presents a new paradigm, which avoids direct data transmission by collaboratively training a global learning model across multiple distributed edge devices, thus overcoming the disadvantage of centralized machine learning in resource limitations, delay constraints, and privacy issues. However, due to the heavy cost of communicating gradient among edge devices, sharing the parameters of a large-scale neural network can still be time-intensive. To alleviate this bottleneck, an efficient scheme, called SignSGD has been recently proposed, where the one-bit gradient quantization with majority vote is featured at edge devices. Nevertheless, the performance of one-bit aggregation will inevitably deteriorate due to the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a novel reconfigurable intelligent surface (RIS)-aided one-bit communication optimization scheme under orthogonal frequency division multiple access (OFDMA) to relieve the negative influence of communication error on the SignSGD-based FEEL. Specifically, a learning convergence analysis is firstly presented to quantitatively characterize the impact of wireless communication error measured by the union bound on pairwise bit error rate (BER) on the performance of SignSGD-based FEEL. Immediately, a unified communication-learning optimization problem is further formulated to jointly optimize the sub-band assignment strategy, the power allocation vector, and the RIS configuration matrix. Numerical experiments show that the proposed design achieves substantial performance improvement compared with the state-of-the-art approaches.

We analyze the outage performance of a decode-and-forward (DF) relaying system with joint decoding over Nakagami-m fading channels in this paper, where the relay node has simultaneous wireless information and power transfer (SWIPT) capability. The relay node adopts a power splitting (PS) protocol to divide the power from the source node into two parts: information transfer and energy harvesting. The destination node performs maximal ratio combining (MRC) to combine the received signals from the source and the relay nodes. For the above-described system, the outage probability of the DF relaying protocol is derived. A series of Monte Carlo simulations are conducted to verify the accuracy of the theoretical analysis. The simulation results are well matched with the theoretical analyses. Furthermore, we identify the optimal ratio of power splitting to minimize the outage probability. It is shown that the optimal splitting factors are influenced by the various ambient environment such as Nakagami-m factor and geometric gains representing the distances between nodes.