Jag har framgångsrikt handlett 18 examensarbeten av master- och kandidatstudenter. Jag har även hållit föreläsningar om Reinforcement Learning för en masterkurs på DSV, SU. Kursen heter "Data Mining in Computer and System Sciences (DAMI)".

Research Interests

• Multiobjective Optimization
• Federated Learning
• Reinforcement Learning
• Wireless, IoT, and Fog networks
• AI-driven decision-making in Water Distribution Networks.

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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