News and Announcements

  • One-Shot Federated Model Editing for Device Dynamics at the Edge

    October 10, 2026

    Standard federated learning (FL) requires persistent device connectivity across multiple communication rounds, making it unsuitable for dynamic environments such as vehicular networks, where devices frequently join and leave. One-shot FL offers a promising alternative by completing training in a single communication round. However, one-shot FL suffers from model solidification and cannot readily adapt to subsequent client dynamics, including new client contributions and the withdrawal of previous contributions. We therefore consider exact model editing, which removes withdrawn contributions through unlearning or incorporates newly contributed ones through incremental learning without retraining from scratch. We identify that efficient editing fundamentally hinges on learning stability. To this end, we propose F-SOSA, the first stable one-shot FL algorithm that enables rapid and exact model editing. F-SOSA leverages sub-sampling of local models with carefully tuned rate for stable aggregation and performs curvature-aware model fusion to preserve utility. We prove that F-SOSA achieves vanishing training error and reasonably small generalization error bounds. Building on this stability, we further develop two single-round editing algorithms: F-SOSA-U for unlearning and F-SOSA-I for incremental learning. They ensure that the edited model, along with all intermediate algorithmic states, is statistically indistinguishable from retraining while incurring only an expected O(ρ) fraction of its cost, where ρ quantifies stability. Experiments demonstrate that our approach matches retraining performance while reducing cumulative time and communication overhead by at least 36% and 22%, respectively, under realistic urban mobility simulations.
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  • Mobility-Aware Interference-Coupled ISAC for Energy-Efficient Multi-Vehicle Tracking

    September 15, 2026

    Integrated sensing and communications (ISAC) has emerged as a promising technology for future vehicular networks by enabling simultaneous wireless communication and environmental sensing using shared spectrum and hardware resources. However, realizing energy-efficient multi-vehicle operation in millimeter wave (mmWave) environments remains challenging due to high mobility, inter-vehicle interference, and the strong coupling between sensing and communication performance. In this paper, we propose a mobility-aware interference-coupled ISAC framework for multi-vehicle tracking in mmWave vehicle-to-infrastructure networks. Accurate sensing and tracking of vehicle states enables efficient predictive beamforming, while the associated transmit power allocation is jointly optimized for energy efficiency under communication quality-of-service and sensing reliability requirements. To accurately capture dense vehicular deployments, inter-vehicle interference is explicitly modeled in both radar sensing and communication links, resulting in a non-convex fractional optimization problem. An efficient alternating optimization framework combining Dinkelbach's method and successive convex approximation is developed to solve the resulting problem with manageable computational complexity. Simulation results demonstrate that the proposed framework substantially improves energy efficiency while maintaining accurate multi-vehicle tracking under realistic interference conditions. Compared with existing predictive beamforming approaches, the proposed scheme achieves higher average energy efficiency and robust tracking performance across varying traffic densities, mobility levels, and noise conditions. Furthermore, the results reveal a fundamental trade-off between sensing reliability and energy efficiency that becomes increasingly pronounced in dense vehicular scenarios, highlighting the importance of interference-aware resource optimization for next-generation ISAC-enabled transportation systems.
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  • Wi-Lo++: WiFi-to-LoRa Cross-Technology Communication with Multi-Packet Emulation

    August 31, 2026

    LoRa was developed for long-range communication in internet of things (IoT) applications, also in the 2.4 GHz ISM band. However, native LoRa support remains limited in mobile devices like smartphones. Cross-technology communication (CTC) addresses this gap by enabling direct over-the-air communication among different communication technologies without requiring additional radio hardware. This work presents Wi-Lo++, a WiFi-to-LoRa CTC mechanism. In contrast to existing WiFi-to-LoRa CTC approaches, it removes the constraint of the maximum packet length of WiFi and therefore can emulate LoRa transmissions with higher spreading factors (SFs). This is achieved by emulating a single LoRa frame through a train of consecutive IEEE 802.11b frames, each emulating only a part of the overall LoRa waveform while preserving symbol-level continuity across packet boundaries. In order to mitigate waveform distortions caused by the variety of immutable WiFi preambles and headers, we shift these distortions into less critical regions of the LoRa waveform. Using a second WiFi interface, these distortions can be further reduced further using overlapping WiFi frames. Experiments using software-defined radio (SDR) for WiFi and COTS LoRa hardware reveal that Wi-Lo++ achieves the same performance as classical LoRa. Finally, a prototype implemented using commercially available off-the-shelf (COTS) WiFi hardware demonstrates the practical feasibility of our approach, achieving an outdoor over-the-air communication range of up to 856 m towards an unmodified LoRa receiver. Our Wi-Lo++ framework is released as open-source for SF<7.
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  • Decentralized Model Predictive Control for Platooning: Enhancing Human-Driver Collaboration

    July 17, 2026

    Recent advances in cooperative adaptive cruise control have demonstrated the potential for vehicle platooning to revolutionize road transportation through enhanced safety, reduced congestion, and improved energy efficiency. While autonomous vehicle technology continues to evolve rapidly, current regulatory frameworks and safety considerations necessitate the human driver supervision. This creates a unique challenge in developing control systems that can effectively balance autonomous operation with human intervention. To enhance the human-driver collaboration with autonomous vehicle platooning, in this paper, we present a novel decentralized model predictive control framework that explicitly incorporates human-driver interaction while maintaining desired inter-vehicle distances and velocities in platoon formations. This framework employs a distributed architecture where each vehicle operates independently and exchanges local measurements through vehicle-to-vehicle communication. To overcome the inherent unreliability of wireless communications in real-world scenarios, we develop a robust distributed state estimation strategy. This approach enables each vehicle to combine local sensor measurements with received data to construct accurate estimates of the full platoon state. Based on these estimates, vehicles compute optimal control actions locally while achieving performance comparable to an ideal centralized controller with perfect communication. Through extensive Plexe simulations, we demonstrate that the proposed decentralized model predictive controller achieves comparable performance to the ideal centralized case, even under partial state information and communication constraints.
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  • Communicating Smartly in Molecular Communication Environments: Neural Networks in the Internet of Bio-Nano Things

    June 10, 2026

    Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the foundation for innovative healthcare applications. Nanodevices designed to operate within the human body and managed remotely via the Internet are envisioned to detect and respond to diseases promptly. To explore the limits of nanodevice interconnectivity, this survey focuses on data-driven communication strategies for molecular communication (MC) systems interconnecting nanosensors. Due to the complex and dynamic nature of MC environments, accurate physical modeling is often infeasible. Consequently, the MC research community increasingly relies on machine learning (ML) methods, particularly neural network (NN) architectures, to enable robust and adaptive communication at the nanoscale level. This interdisciplinary field spans several aspects, including NNs for communication in IoBNT networks, their nanoscale implementation, explainable approaches, and the generation of training datasets. Within this survey, we provide a comprehensive analysis of current NN architectures for MC, assess their feasibility for nanoscale deployment, review applied explainable artificial intelligence (XAI) techniques, and summarize available datasets along with best practices for their generation. We also include open-source code examples to support reproducible research across key MC scenarios. Finally, we identify emerging challenges, including robust NN architectures, biologically integrated NN modules, and scalable training strategies.
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  • Fed-RAA: Resource-Adaptive Asynchronous Federated Edge Learning with Theoretical Guarantee

    May 21, 2026

    This paper studies an efficient federated learning (FL) problem involving multiple edge-based clients with heterogeneous constrained resources. Compared with numerous training parameters, the computing and communication resources of clients in edge scenarios are usually insufficient for fast local training and real-time knowledge sharing. Besides, training on clients with heterogeneous resources may result in the straggler problem, which delays the convergence of FL. To address these issues, we propose Fed-RAA: a Resource-Adaptive Asynchronous Federated learning algorithm. Different from vanilla FL methods, where all parameters are trained by each participating client regardless of resource diversity, Fed-RAA adaptively allocates submodels of the global model to clients based on their computing and communication capabilities. Each client then individually trains its assigned submodel and asynchronously uploads the updated result. Theoretical analysis confirms the convergence of our approach. Additionally, an online greedy-based algorithm is designed for asynchronous submodel assignment in Fed-RAA, improving the convergence of Fed-RAA by optimal minimization on the training delay bound of submodels. Compared to state-of-the-art methods, our Fed-RAA algorithm reduces the time required to achieve the target accuracy by an average of 30.89%, demonstrating its superior efficiency on heterogeneous constrained computing and communication resources. To the best of our knowledge, this paper is the first resource-adaptive asynchronous method for submodel-based FL with guaranteed theoretical convergence.
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  • Byzantine-Resilient Federated Learning under Heterogeneity and Heavy Tails

    April 24, 2026

    Byzantine resilience is essential in federated learning (FL) to safeguard model training from malicious or faulty participants. However, existing Byzantine-resilient methods struggle when faced with heavy-tailed gradient noise, a common challenge in heterogeneous environments. In this work, we propose a Byzantine-resilient FL framework specifically designed to handle both heterogeneity and heavy-tailed noise. Our approach builds on robust distributed stochastic heavy-ball optimization, incorporating update normalization and gradient/momentum clipping to mitigate the effects of heavy-tailed noise. We establish the first high-probability convergence guarantees for Byzantine-resilient FL under these conditions, showing that our algorithms achieve optimal Byzantine resilience and align with known lower bounds. Additionally, we introduce an efficient variant of the nearest neighbor mixing technique, leveraging random projections to significantly reduce computational costs in high-dimensional settings. Through rigorous theoretical analysis and extensive empirical evaluations, we demonstrate that our methods outperform existing approaches in robustness against both Byzantine failures and heavy-tailed noise.
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  • Hierarchical Federated Learning in Device-to-Device Networks with Learning-Topology Co-Optimization

    March 25, 2026

    Federated learning (FL) enables collaborative model training across distributed devices while preserving privacy. However, growing heterogeneity in device resources and communication links challenges conventional FL, especially when relying on a single central server. Hierarchical federated learning (HFL) mitigates these issues by organizing devices into clusters coordinated through intermediate aggregators. Yet, the effectiveness of HFL critically depends on how clusters are formed: intra-cluster communication must be efficient, device computational capacities should be balanced to reduce stragglers, and data heterogeneity must be managed to ensure stable convergence. In this work, we propose a learning–topology co-optimization framework for HFL in networks where nodes communicate with each other with links of varying quality (e.g., device-to-device (D2D) or mesh networks). Our method jointly optimizes device connection topology and learning directions, leading to communication-efficient clusters that remain well aligned in optimization space. We provide a convergence analysis under mild assumptions, showing how inter- and intra-cluster divergence affect learning stability.
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  • Blood Makes a Difference: Experimental Evaluation of Molecular Communication in Different Fluids

    February 17, 2026

    The experimental appraisal of existing molecular communication (MC) testbeds and modeling frameworks in real blood is an important step for future internet of bio-nano-things applications. In this paper, we experimentally compare the MC flow characteristics of water, blood substitute, and real porcine blood for a previously presented superparamagnetic iron oxide nanoparticles (SPION) MC testbed. We perform an extensive analysis of the system impulse response behavior of the testbed for the different fluids. Based on the identified MC flow characteristics, we extend an existing mathematical framework for our SPION testbed to capture the flow properties of blood. We evaluate its applicability to the collected data in comparison to two existing theoretical SIR models for MC in blood. In our work, we see that the added complexity of the transmission in blood opens up promising new possibilities to improve communication through the human circulatory system.
    • Lisa Y. Debus, Mario J. Wilhelm, Henri Wolff, Luiz C. P. Wille, Tim Rese, Michael Lommel, Jens Kirchner and Falko Dressler, "Blood Makes a Difference: Experimental Evaluation of Molecular Communication in Different Fluids," IEEE Transactions on Molecular, Biological and Multi-Scale Communications, August 2025. (online first) [DOI, BibTeX, More details]

  • Rejuvenating IRS: AoI-based Low Overhead Reconfiguration Design

    January 27, 2026

    Intelligent reflective surface (IRS) technologies help mitigate undesirable effects in wireless links by steering the communication signal between transmitters and receivers. IRS elements are configured to adjust the phase of the reflected signal for a user's location and enhance the perceived signal-to-noise ratio (SNR). In this way, an IRS improves the communication link but inevitably introduces more communication overhead. This occurs especially in mobile scenarios, where the user's position must be frequently estimated to re-adjust the IRS elements periodically. Such an operation requires balancing the amount of training versus the data time slots to optimize the communication performance in the link. Aiming to study this balance with the age of information (AoI) framework, we address the question of how often an IRS needs to be updated with the lowest possible overhead and the maximum of freshness of information. We derive the corresponding analytical solution for a mobile scenario, where the transmitter is static and the mobile user (MU) follows a random waypoint mobility model. We provide a closed-form expression for the average peak age of information (PAoI), as a metric to evaluate the impact of the IRS update frequency. As for the performance evaluation, we consider a realistic scenario following the IEEE 802.11ad standard, targeting the mmWave band. Our results reveal that the minimum achievable average PAoI is in the microsecond range and the optimal IRS update period is in the seconds range, causing 9 % overhead in the link when the MU moves at a velocity of 1 m/s.
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  • Redundancy in WiFi 7: Combining Multi-link Operation with IEEE 802.1CB FRER

    December 06, 2025

    The increasing complexity of modern industrial and manufacturing systems, featuring numerous sensors and mobile components, demands reliable, low-latency communication over wireless networks. WiFi 7 addresses these requirements through enhancements such as Multi-Link Operation (MLO), enabling simultaneous use of multiple frequency bands and offering inherent link diversity. This raises the question of whether MLO can be effectively leveraged for reliability in critical systems. In this paper, we explore the integration of IEEE 802.1CB Frame Replication and Elimination for Reliability (FRER), a core Time-Sensitive Networking (TSN) standard, over MLO to address this question. We present an open-source implementation of FRER over MLO in the OMNeT++ simulator, highlight key challenges in combining these technologies, and evaluate its effectiveness in improving reliability under wireless-specific conditions such as mobility and congestion. Our results demonstrate that FRER can enhance packet delivery ratio and ensure bounded latency, albeit at the expense of reduced channel efficiency due to redundancy.
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  • Operator Coexistence in IRS-Assisted mmWave Networks: A Wideband Approach

    November 09, 2025

    There are multiple challenges associated with IRS, which have to be addressed before full incorporation of this technology into existing networks. A key issue arises from the inability of IRS to filter out non-target signals from other frequency bands due to lack of bandpass filtering. In areas where multiple wireless operators are spatially nearby, even if they use different frequency bands, this may cause unwanted reflections that may degrade their communication performances. To address this challenge, we previously proposed a solution, which relied on partitioning an IRS into sub-surfaces (sub_IRS) and dynamically assigning operators to these sub_IRS. Results have shown that a proper assignment of wireless operators to sub_IRS can improve the overall performance compared to a random assignment. In this paper, we introduce a wideband approach, demonstrating that the impact from unwanted reflections can be mitigated by using wideband channels, as the average signal to noise ratio (SNR) across subcarriers is less adversely affected. This approach leverages frequency diversity to reduce SNR variance, as some of the subcarriers may be negatively affected while others benefit, resulting in maintaining a more consistent and robust system performance in the presence of IRS-induced unwanted reflections.

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