Federated Learning Final Year Projects with Source Code

Byadmin Final Year Projects

Federated Learning Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Federated Learning projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Federated Learning Final Year Projects

  1. A Systematic Review on Federated Learning in Medical Image Analysis
    This project reviews how Federated Learning (FL) is used for analyzing medical images while keeping patient data private. The authors collected and studied research articles to understand how FL models perform compared to traditional methods. They summarized the current methods, results, challenges, and suggested directions for future research. Overall, it gives a clear picture of FL’s role in privacy-preserving medical AI.
  2. Attack Detection for Medical Cyber-Physical SystemsA Systematic Literature Review
    This project looks at cyber attacks in hospitals, focusing on medical cyber-physical systems, which include devices connected to hospital networks. The researchers reviewed existing studies to understand how intrusions are detected, what datasets are used, and what gaps exist. They found that most work focuses on detecting unusual activity at the network level, often targeting insider threats. The study suggests creating specialized hospital datasets, improving standards, and developing methods that use medical context to better prevent cyber attacks and protect patients.
  3. Securing IoT With Deep Federated Learning A Trust-Based Malicious Node Identification Approach
    This project builds a system that helps smart devices identify which devices in a network can be trusted. It uses learning from many devices together without sharing their data. The system can detect unusual or harmful behavior in the network. This helps improve safety and privacy in Internet of Things environments.
  4. Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance A Review
    This project explores how artificial intelligence can use large amounts of medical data from sources like wearable sensors, medical images, and health records. It shows how AI can help with disease diagnosis, monitoring body signals, and delivering personalized treatments. The study also highlights new computing tools like cloud, GPUs, and edge devices that make this possible. Finally, it discusses challenges in handling medical data and the future of AI-driven healthcare.
  5. Implementing Privacy-Preserving and Collaborative Industrial Artificial Intelligence
    This project focuses on improving industrial AI for smart manufacturing. It uses a method called federated learning, which allows different factories or systems to train AI models together without sharing raw data, keeping it private and secure. The project also develops a framework to guide collaborative AI use in industry. Additionally, it provides a publicly available dataset for quality inspection to help further research.
  6. Byzantine-Resilient Secure Federated Learning on Low-Bandwidth Networks
    This project focuses on improving secure and reliable machine learning where multiple users collaborate without sharing raw data. It makes federated learning faster by letting a semi-trusted server help verify users’ data securely. The approach reduces communication and computation time, especially on slow networks like mobile connections. It still protects against faulty or malicious users while keeping their data private.
  7. Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking
    This project studies a way to train machine learning models without a central server, using devices that share updates with their neighbors. It focuses on keeping data private while still achieving high accuracy. The system is tested on the MNIST image dataset using a CNN model, and it shows over 95% accuracy. The method also reduces communication costs and trains faster compared to traditional centralized systems.
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At Final Year Projects, we provide complete guidance for Federated Learning IEEE projects for BE, BTech, ME, MSc, MCA and MTech students. We assist at every step from topic selection to coding, report writing, and result analysis.

Our team has over 10 years of experience guiding students in Computer Science, Electronics, Electrical, and other engineering domains. We support students across India, including Hyderabad, Mumbai, Bangalore, Chennai, Pune, Delhi, Ahmedabad, Kolkata, Jaipur and Surat. International students in the USA, Canada, UK, Singapore, Australia, Malaysia, and Thailand also benefit from our expert guidance.

Federated Learning Project Synopsis & Presentation

Final Year Projects helps prepare Federated Learning project synopsis, including problem statement, objectives, existing system, disadvantages, proposed system, advantages and research motivation. We provide PPT slides, tutorials, and full documentation for presentations.

Federated Learning Project Thesis Writing

Final Year Projects provides thesis writing services for Federated Learning projects. We help BE, BTech, ME, MSc, MCA and MTech students complete their final year project work efficiently.

All theses are checked with plagiarism check tools to guarantee originality and quality. Fast-track services are available for urgent submissions. Hundreds of students have successfully completed their projects and theses with our support.

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We offer complete support for Federated Learning research papers. Services include writing, editing, and proofreading for journals and conferences.

We accept Word, RTF, and LaTeX formats. Every paper is reviewed to meet IEEE and publication standards, improving acceptance chances. Our guidance ensures that students produce high-quality, publication-ready research papers.

Reach out to Final Year Projects for expert guidance on Federated Learning projects. Get support for coding, reports, theses, and research publications. Contact us via email, phone, or website form and start your project with confidence.

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