Jag har framgångsrikt handlett 27 examensarbeten för master- och kandidatstudenter. Jag har även hållit föreläsningar om förstärkande lärande och klusterbildning för masterkurser vid DSV, SU. Jag har även bidragit till att genomföra laborationer, handledningstillfällen och betygsätta studenter.

Research Interests

• Multiobjective Optimization
• Federated Learning
• Reinforcement Learning
• Wireless, IoT, and Fog networks
• AI-driven Resource-Adaptive decision-making in IoT

I urval från Stockholms universitets publikationsdatabas

• Energy-Efficient and Adaptive Gradient Sparsification for Federated Learning

  2023. Shubham Vaishnav, Maria Efthymiou, Sindri Magnússon. IEEE International Conference on Communications (ICC), 2023, 1256-1261

  Konferens

  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.

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• Intelligent Processing of Data Streams on the Edge Using Reinforcement Learning

  2023. Shubham Vaishnav, Sindri Magnússon. 2023 IEEE International Conference on Communications Workshops (ICC Workshops)

  Konferens

  A key challenge in many IoT applications is to en-sure energy efficiency while processing large amounts of streaming data at the edge. Nodes often need to process time-sensitive data using limited computing and communication resources. To that end, we design a novel R - Learning based Offloading framework, RLO, that allows edge nodes to learn energy optimal decisions from experience regarding processing incoming data streams. In particular, when should the node process data locally? When should it transmit data to be processed by a fog node? And when should it store data for later processing? We validate our results on both real and simulated data streams. Simulation results show that RLO learns with time to achieve better overall-rewards with respect to three existing baseline schemes. Moreover, the proposed algorithm excels the existing baseline schemes when different priorities were set on the two objectives. We also illustrate how to adjust the priorities of the two objectives based on the application requirements and network constraints.

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• Communication-Adaptive Gradient Sparsification for Federated Learning with Error Compensation

  2025. Shubham Vaishnav, Sarit Khirirat, Sindri Magnússon. IEEE Internet of Things Journal 12 (2), 1137-1152

  Artikel

  Federated learning 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 Federated Learning 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 Federated Learning 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 non-convex 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 proposed FL-CATE algorithm can substantially improve the communication efficiency of federated learning, irrespective of the ML tasks, costs, and communication models.

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• Multiobjective and Constrained Reinforcement Learning for IoT

  2024. Shubham Vaishnav, Sindri Magnússon. Learning techniques for Internet of Things, 153-170

  Kapitel

  IoT networks of the future will be characterized by autonomous decision-making by individual devices. Decision-making is done with the purpose of optimizing certain objectives. A multitude of mathematically oriented algorithms exist for solving optimization problems. However, optimization in IoT networks is challenging due to a number of uncertainties, complex network topologies, and rapid changes in the environment. This makes the data-driven and machine learning (ML) approaches more suitable for effectively handling IoT environments’ dynamic and intricate nature. However, supervised and unsupervised ML approaches depend on training data, which is not always available before training. In recent years, reinforcement learning (RL) has attracted considerable attention for solving optimization problems in IoT. This is because RL has the distinguishing feature of learning with experience while interacting with the environment without training data. A central challenge in decision-making in IoT networks is that most optimization problems consist of co-optimizing multiple conflicting objectives. With the development of multi-objective RL (MORL) approaches over the last two decades, there is great potential for utilizing them for future IoT networks. Most recently developed MORL approaches have not been applied in the IoT domain. In this chapter, we will discuss the need for efficient multi-objective optimization in IoT, the fundamentals of using RL for decision-making in IoT, an overview of existing MORL approaches, and, finally, the future scope and challenges associated with utilizing MORL for IoT.

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