IoT networks often face conflicting routing goals such as maximizing packet delivery, minimizing delay, and conserving limited battery energy. These priorities can also change dynamically: for example, an emergency alert requires high reliability, while routine monitoring prioritizes energy efficiency to prolong network lifetime. Existing works, including many deep reinforcement learning approaches, are typically centralized and assume static objectives, making them slow to adapt when preferences shift. We propose a dynamic and fully distributed multi-objective Q-learning routing algorithm that learns multiple per-preference Q-tables in parallel and introduces a novel greedy interpolation policy to act near-optimally for unseen preferences. The algorithm learns to optimize for energy efficiency, packet delivery ratio, and the composite reward, adapting to changing trade-offs between these metrics without retraining or centralized control. A theoretical analysis further shows that the optimal value function is Lipschitz-continuous in the preference parameter, ensuring that proposed greedy interpolation policy yields provably near-optimal behavior. Simulation results show that our approach adapts in real time to shifting priorities and achieves up to 80–90% lower energy consumption and up to 5 × higher cumulative rewards and packet delivery compared to six baseline protocols, under dynamic and distributed settings. Sensitivity analysis across varying preference window lengths confirms that the proposed DPQ framework consistently achieves higher composite reward than all baseline methods, demonstrating robustness to changes in operating conditions.

Internet of Things (IoT) systems increasingly operate in environments where devices must respond in real time while managing fluctuating resource constraints, including energy and bandwidth. Yet, current approaches often fall short in addressing scenarios where operational constraints evolve over time. To address these limitations, we propose a novel Budgeted Multi-Armed Bandit framework tailored for IoT applications with dynamic operational limits. Our model introduces a decaying violation budget, which permits limited constraint violations early in the learning process and gradually enforces stricter compliance over time. We present the Budgeted Upper Confidence Bound (UCB) algorithm, which adaptively balances performance optimization and compliance with time-varying constraints. We provide theoretical guarantees showing that Budgeted UCB achieves sublinear regret and logarithmic constraint violations over the learning horizon. Extensive simulations in a wireless communication setting show that our approach achieves faster adaptation and better constraint satisfaction than standard online learning methods. These results highlight the framework’s potential for building adaptive, resource-aware IoT systems.

Federated learning (FL) has emerged as a popular distributed machine-learning paradigm. It involves many rounds of iterative communication between nodes to exchange model parameters. With the increasing complexity of ML tasks, the models can be large, having millions of parameters. Moreover, edge and IoT nodes often have limited energy resources and channel bandwidths. Thus, reducing the communication cost in FL is a bottleneck problem. This cost could be in terms of energy consumed, delay involved, or amount of data communicated. We propose a communication cost-adaptive model sparsification for FL with error compensation. The central idea is to adapt the sparsification level in run-time by optimizing the ratio between the impact of the communicated model parameters and communication cost. We carry out a detailed convergence analysis to establish the theoretical foundations of the proposed algorithm. We conduct extensive experiments to train both convex and nonconvex machine learning models on a standard dataset. We illustrate the efficiency of the proposed algorithm by comparing its performance with three baseline schemes. The performance of the proposed algorithm is validated for two communication models and three cost functions. Simulation results show that the proposed algorithm needs a substantially less amount of communication than the three baseline schemes while achieving the best accuracy and fastest convergence. The results are consistent for all the considered cost models, cost functions, and ML models. Thus, the proposedFL-CATEalgorithm can substantially improve the communication efficiency of FL, irrespective of the ML tasks, costs, and communication models.

Federated learning is an emerging machine-learning technique that trains an algorithm across multiple decentralized edge devices or clients holding local data samples. It involves training local models on local data and uploading model parameters to a server node at regular intervals to generate a global model which is transmitted to all clients. However, edge nodes often have limited energy resources, and hence performing energy-efficient communication of model parameters is a bottleneck problem. We propose an energy-adaptive model sparsification for Federated Learning. The central idea is to adapt the sparsification level in run-time by optimizing the ratio between information content and energy cost. We illustrate the efficiency of the proposed algorithm by comparing its performance with three baseline schemes. We validate the performance of the proposed algorithm for two cost models. Simulation results show that the proposed algorithm needs exponentially less amount of communication and energy as compared to the three baseline schemes while achieving the best accuracy and fastest convergence.