Fuzzified Federated Multi-Task Unlearning for Efficient and Privacy-Preserving Swarm Consumer Electronics Systems

The proliferation of consumer electronics systems in swarm environments has heightened the need for robust privacy-preserving techniques in federated learning (FL) frameworks. Traditional FL methods, while effective in distributing model training, often fall short in efficiently handling data removal requests and safeguarding against inference attacks. Existing solutions, such as FL with Differential Privacy (FL+DP), offer some improvements but still face challenges in balancing performance, privacy, and communication efficiency. In this paper, we introduce FL+Fuzzy Unlearning, a novel FL framework that integrates Fuzzy Logic to address these challenges in federated multi-task learning for swarm consumer electronics systems. Our method aims to maintain high model performance while ensuring robust privacy and efficient communication. We evaluated FL+Fuzzy Unlearning against Traditional FL and FL+DP using two benchmark datasets: MNIST and MovieLens. Our results demonstrate that the proposed approach attains comparable or superior accuracy (0.86 on MNIST and 0.88 on MovieLens) while achieving robust unlearning performance. Moreover, FL+Fuzzy Unlearning substantially enhances privacy protection, evidenced by elevated inference attack failure rates (0.75 for MNIST and 0.78 for MovieLens), and effectively reduces communication overhead, underscoring its efficiency in data exchange. Overall, FL+Fuzzy Unlearning presents a promising, balanced solution for privacy, efficiency, and adaptability, making it highly suitable for real-world applications in swarm consumer electronics systems.