Fenil Ramoliya

Abstract Transformer models have demonstrated remarkable success in natural language processing (NLP) tasks, but their deployment in distributed environments faces critical challenges including high computational demands, large memory footprints, and privacy concerns when handling sensitive data. Existing federated learning (FL) implementations with transformers often sacrifice either model performance for privacy or resource efficiency for accuracy.

In emerging nations, electricity as an energy carrier can be utilized to fulfil the people’s increasing travelling demand globally. Authentic and dependable electrical power transfer is the foundation of a country’s economic growth. According to the Annual Energy Outlook, the United States (US) is going to witness 31% growth in electricity demand by 2035. Towards this goal, people are incorporating electric vehicles (EVs) into their modernized transportation systems across the globe [1].