Subhrakanti Dey

Federated learning in vehicular edge networks faces major challenges in efficient resource allocation, largely due to high vehicle mobility and the presence of imperfect channel state information. Many existing methods oversimplify these realities, often assuming fixed communication rounds or ideal channel conditions, which limits their effectiveness in real-world scenarios. To address this, we propose variable rate vehicular federated learning (VR-VFL), a novel federated learning method designed specifically for vehicular networks under imperfect channel state information. VR-VFL combines dynamic client selection with adaptive transmission rate selection, while also allowing round times to flex in response to changing wireless conditions. At its core, VR-VFL is built on a bi-objective optimization framework that strikes a balance between improving learning convergence and minimizing the time required to complete each round. By accounting for both the challenges of mobility and realistic wireless constraints, VR-VFL offers a more practical and efficient approach to federated learning in vehicular edge networks. Simulation results show that the proposed VR-VFL scheme achieves convergence approximately 40% faster than other methods in the literature.

This article investigates remote state estimation in cyber–physical systems (CPSs) where direct and reliable communication between distributed sensors and the control center is unavailable due to the lack of infrastructure. In such scenarios, uncrewed aerial vehicles (UAVs) can be deployed as mobile data collectors to facilitate information transfer. While joint UAV trajectory and resource allocation problems have been widely studied in various contexts, adapting these to minimize estimation error in remote state estimation of linear CPS—accounting for the nonlinear evolution of error covariances and spatiotemporal coupling—presents distinct challenges. To tackle this, we formulate a joint optimization problem and decompose it into UAV path planning and UAV–sensor resource allocation. The path planning problem is modeled as a Markov decision process (MDP), where we establish the existence and periodicity of the optimal policy, and the resource allocation is formulated as a many-to-one matching game. We jointly solve these subproblems via an alternating optimization algorithm that combines dynamic programming with a deferred acceptance-based matching scheme, and we prove the stability of the resulting matching. Simulation results confirm the periodic structure of the optimal path and demonstrate the effectiveness of the proposed method in reducing estimation error compared with other representative matching-based schemes.

Distributed non-cooperative games are prevalent in emerging applications such as traffic control, vehicle charging, and smart grid management. In distributed systems without central coordinators, agents must share and retrieve information locally to seek a Nash equilibrium (NE). However, this extensive data exchange can lead to significant communication bottlenecks. To address this challenge, over-the-air computing provides a promising solution by exploiting the superposition property of wireless multiple access channels (MAC), allowing for substantial bandwidth savings. In this paper, we propose an over-theair framework for general distributed non-cooperative games. Specifically, we introduce an algorithm based on non-coherent over-the-air computing, AirNES, to find an NE in distributed non-cooperative games. Our algorithm accounts for noisy channels and non-coherent transmission, eliminating the need for channel state information. We demonstrate that, with properly tuned decreasing consensus and gradient stepsizes, AirNES guarantees almost sure convergence to the exact NE, even in the presence of channel fading and additive noise. Additionally, we extend our analysis to scenarios with fixed stepsizes, where linear convergence can be achieved at the expense of reduced accuracy. Finally, we provide numerical simulations to demonstrate the effectiveness of the proposed protocol.

Neuromorphic computing is an emerging technology enabling low-latency and energy-efficient signal processing. A key algorithmic tool in neuromorphic computing is spiking neural networks (SNNs). SNNs are biologically inspired neural networks which utilize stateful neurons, and provide low-bit data processing by encoding and decoding information using spikes. Similar to SNNs, deep state-space models (SSMs) utilize stateful building blocks. However, deep SSMs, which recently achieved competitive performance in various temporal modeling tasks, are typically designed with high-precision activation functions and no reset mechanisms. To bridge the gains offered by SNNs and the recent deep SSM models, we propose a novel multiple-output spiking neuron model that combines a linear, general SSM state transition with a non-linear feedback mechanism through reset. Compared to the existing neuron models for SNNs, our proposed model clearly conceptualizes the differences between the spiking function, the reset condition and the reset action. The experimental results on various tasks, i.e., a keyword spotting task, an event-based vision task and a sequential pattern recognition task, show that our proposed model achieves performance comparable to existing benchmarks in the SNN literature. Our results illustrate how the proposed reset mechanism can overcome instability and enable learning even when the linear part of neuron dynamics is unstable, allowing us to go beyond the strictly enforced stability of linear dynamics in recent deep SSM models.

Neuromorphic computing is an emerging technology enabling low-latency and energy-efficient signal processing. A key algorithmic tool in neuromorphic computing is spiking neural networks (SNNs). SNNs are biologically inspired neural networks which utilize stateful neurons, and provide low-bit data processing by encoding and decoding information using spikes. Similar to SNNs, deep state-space models (SSMs) utilize stateful building blocks. However, deep SSMs, which recently achieved competitive performance in various temporal modeling tasks, are typically designed with high-precision activation functions and no reset mechanisms. To bridge the gains offered by SNNs and the recent deep SSM models, we propose a novel multiple-output spiking neuron model that combines a linear, general SSM state transition with a non-linear feedback mechanism through reset. Compared to the existing neuron models for SNNs, our proposed model clearly conceptualizes the differences between the spiking function, the reset condition and the reset action. The experimental results on various tasks, i.e., a keyword spotting task, an event-based vision task and a sequential pattern recognition task, show that our proposed model achieves performance comparable to existing benchmarks in the SNN literature. Our results illustrate how the proposed reset mechanism can overcome instability and enable learning even when the linear part of neuron dynamics is unstable, allowing us to go beyond the strictly enforced stability of linear dynamics in recent deep SSM models

In wireless networked control systems, a common challenge arises in deciding between a remote controller, with higher computational power but delayed information due to wireless communication, and a local onboard controller, with limited computing resources but timely feedback. This letter investigates such a trade-off in the context of the Linear Quadratic Gaussian (LQG) control, focusing on the interplay between computational power and communication delay. We compare two typical controllers: a local controller operating at a coarse control rate and a remote controller utilizing a finer control rate but experiencing network delay. A formal analytical framework is proposed to quantify the impact of network delay on the LQG cost and computation resources. We show that there exists a critical communication delay above which the more powerful remote controller is no more convenient with respect to the simpler local controller, providing a guideline for controller selection in varying network conditions with fixed computing resource configurations. Our approach provides a lightweight, interpretable selection rule based on the communication-computation trade-off. Simulation results validate the effectiveness of our theoretical insights.