Unveiling The Tapestry: Federated Learning Challenges and Opportunities in The Indian Educational Landscape

Abstract

In recent times, Federated Learning (FL) has positioned itself as a cornerstone of decentralized machine learning, providing unparalleled benefits in terms of data protection, security, and edge computations. Instead of consolidating data for model training, FL advocates for learning across a multitude of devices, underscoring a transformative approach to large-scale machine learning endeavours. However, this innovative method is not devoid of challenges. In the landscape of the Indian education system, Federated Learning (FL) emerges as a pivotal paradigm for decentralized machine learning, offering unprecedented advantages in data protection, security, and edge computations. This transformative approach advocates for learning across diverse devices, reshaping large-scale machine learning practices. However, FL confronts multifaceted challenges. This review scrutinizes technical, security, and pragmatic obstacles within the Indian educational context. Technical intricacies, including non-IID data impact and communication bottlenecks, are explored, alongside security threats like adversarial model tampering. Real-world hurdles encompass varying device capabilities, intermittent connectivity, and device laggardness. By offering a comprehensive perspective, this review aims to guide researchers and industry professionals toward resilient solutions, fostering broader integration of federated learning in diverse educational applications within India.