Federated Learning for Edge Intelligence
Prof. Prajakta Yadav
Assist prof in Dept.Computer Science & Design Engineering
New horizon institute of technology and management
Thane, India prajaktayadav@nhitm.ac.in
Mr. Pratik Yalameli
Computer Science & Design Engineering
New horizon institute of technology and management
Thane, India pratikyalameli217@nhitm.ac.in
Mr. Monish Parulekar
Computer Science & Design EngineeringNew horizon institute of technology and management
Thane, India monishparulekar217@nhitm.ac.in
Mr.Akarshan Shukla
Computer Science and
Design Engineering
New horizon institute of technology and management
Thane, India akarshanshukla217@nhitm.ac.in
Miss.Chanchal Singhal
Computer Science & Design Engineering
New horizon institute of technology and management
Thane, India chanchalsinghal217@nhitm.ac.in
Prof. Khushboo Singh
Assist prof in Dept.Computer Science & Design Engineering
New horizon institute of technology and management
Thane, India kshushboosingh@nhitm.ac.in
Abstract— This research investigates the comparative performance of federated and centralized learning models for bird image classification across multiple training rounds. We implement a complete federated learning system using TensorFlow and Flower framework, with a MobileNetV2-based architecture capable of classifying five categories (bluetit, jackdaw, robin, unknown_bird, unknown_object). Our system demonstrates that federated learning achieves 92.3% accuracy compared to 94.1% in centralized mode, with the added benefit of data privacy preservation. The implementation includes a web-based interface for real-time classification, model statistics visualization, and prediction history tracking. Key findings show that while centralized models maintain slightly higher accuracy, federated models exhibit competitive performance (within 2% accuracy difference) with significant privacy advantages, making them suitable for ecological monitoring applications where data sharing is restricted.
Keywords:Federated Learning, Centralized Learning, Accuracy Comparison, Machine Learning, Data Privacy, Decentralized Systems.
