Federated Learning Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Federated Learning ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.

Federated Learning Projects and Research Topics

Latest Federated Learning Projects

  1. FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource-Constrained Devices Using Divide and Collaborative Training
    This project focuses on making advanced machine learning models usable on devices with limited memory, like smartphones or wearable sensors. Instead of having each device train a big model alone, the method splits the model into smaller parts and lets multiple devices train them together. Devices in a group can also learn from each other, which improves the results. The approach reduces memory needs, speeds up training, and works well on both standard and medical datasets.
  2. Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images
    This project focuses on detecting liver cancer using CT scans. It uses smart computer programs that learn from many hospitals’ data without sharing patient details. The system can accurately find the liver and tumor areas in images. It aims to make cancer detection faster, safer, and more reliable for doctors.
  3. Temporal Action Localization in the Deep Learning Era: A Survey
    This project studies how to automatically find and recognize actions in long, unedited videos. It looks at different ways computers can learn from videos, either with full guidance or limited guidance. The work reviews many existing methods, explains their strengths and weaknesses, and suggests ways to improve accuracy. It helps new researchers understand the field and gives ideas for better action detection in videos.
  4. A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework
    This project uses artificial intelligence to help detect breast cancer from medical images. It keeps patient data private by using federated learning, which trains models without sharing personal data. The system improves accuracy by enhancing images, balancing the data, and using advanced AI models. Tests show it performs better than traditional methods and can be used in healthcare safely.
  5. A Framework for Cognitive, Decentralized Container Orchestration
    This project introduces CODECO, a system that helps decide the best infrastructure for running modern Internet applications on a mix of cloud and edge devices. It uses smart, decentralized methods to handle challenges like weak connections or device failures. CODECO aims to meet user goals like energy efficiency or reliability. The framework is open-source and can be used and tested by researchers.
  6. Advancing UAV Communications: A Comprehensive Survey of CuttingEdge Machine Learning Techniques
    This project studies how machine learning can help drones communicate better and work smarter in mobile networks. It explains how drones can act as flying users or base stations to improve network coverage. The paper reviews different learning methods that help drones save energy and find the best flight paths. It also explores how new AI techniques can connect with cloud and edge systems for better performance.
  7. Federated Learning in Heterogeneous Wireless Networks With Adaptive Mixing Aggregation and Computation Reduction
    This project improves federated learning for devices with different computing powers and network conditions. It uses a new framework called AMA-FES to make training more stable and accurate. Low-power devices only update part of the model to save computation. The system is tested with drones doing image classification and shows better results without extra cost.
  8. Advancing UAV Communications A Comprehensive Survey of CuttingEdge Machine Learning Techniques
    This project reviews how machine learning is used with drones in mobile networks. It explains how drones can act like flying users or mini base stations. The study looks at how AI can help improve coverage, energy use, and network performance. It also explores new AI methods and how they can work with cloud or edge computing.
  9. ChannelComp: A General Method for Computation by Communications
    This project develops a new method called ChannelComp that allows multiple wireless devices to combine their signals digitally at a receiver. Unlike traditional approaches, it works with digital communication, which is more reliable and widely used. The method finds the best way to encode signals for computing functions over the air. Simulations show it performs much better than older techniques, especially for multiplying signals.
  10. Massive MIMO for Serving Federated Learning and Non-Federated Learning Users
    This project focuses on improving future wireless networks that serve two types of users at the same time. It uses federated learning to keep user data private while sending and receiving information efficiently. The study compares two communication methods to see which delivers better speed and reliability. The results show that the proposed methods work better than existing ones, especially when using the full-duplex approach.
  11. Meta Federated Reinforcement Learning for Distributed Resource Allocation
    This project focuses on improving how cellular networks share resources like power and channels. Instead of relying on a central server, users handle much of the computation locally. The method helps save energy, reduce network traffic, and adapt quickly to changing conditions. It also allows users to collaborate, making the system faster and more efficient than traditional approaches.
  12. On-Board Federated Learning for Satellite Clusters With Inter-Satellite Links
    This project studies how networks of low Earth orbit satellites can work together to process data using machine learning directly on the satellites. It proposes a method where satellites share and combine information with each other before sending it to a central server. This approach reduces communication needs and speeds up learning. The result is faster and more efficient satellite data processing.
  13. Precoder Optimization Using Data Correlation for Wireless Data Aggregation
    This project focuses on improving how data is collected from many sensors in a wireless network. It designs a method to send data more accurately while considering how the data from different sensors are related. The approach uses an optimization technique to reduce errors in the collected data. It performs better than older methods, especially when sensor data are closely related.
  14. IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems
    This project focuses on making industrial systems smarter and safer. It develops a model that can detect unusual activities in industrial networks without exposing sensitive data. The approach protects privacy while explaining how decisions are made by the system. Tests show it works well on real industrial data.
  15. Securing Cyber-Physical Systems: A Decentralized Framework for Collaborative Intrusion Detection With Privacy Preservation
    This project focuses on protecting critical systems from cyber-attacks. It studies ways to detect network intrusions using smart learning methods. The approach allows multiple organizations to train a shared detection model without sharing their private data. The results show it can accurately identify attacks while keeping data secure.
  16. QFDSA: A Quantum-Secured Federated Learning System for Smart Grid Dynamic Security Assessment
    This project develops a smart and secure system to check the stability of electrical grids. It uses machine learning to learn from many local data sources without sharing raw data. The system ensures privacy using advanced quantum security techniques. Tests show it works faster and more securely than traditional methods.
  17. A Comparative Study of Lightweight Machine Learning Techniques for Cyber-Attacks Detection in Blockchain-Enabled Industrial Supply Chain
    This project focuses on improving the security of industrial supply chains using modern technology. It combines blockchain and smart sensors to protect systems from cyber-attacks. Machine learning models are used to detect threats early. The study compares different models to find the most effective and efficient one for small devices.
  18. Rethinking Membership Inference Attacks Against Transfer Learning
    This project studies privacy risks in transfer learning, a type of machine learning where knowledge is shared between models. The researchers show that even if someone only has access to the “student” model, they can infer whether specific data was used to train the original “teacher” model. They propose a method to detect this by comparing hidden information inside models. The work highlights a hidden privacy risk in using transfer learning.
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How We Help You with Federated Learning Projects

At UniPhD, we provide complete guidance and support for Federated Learning ieee projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.

We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.

Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.

Federated Learning Thesis and Dissertation Writing

UniPhD has a team of experienced academic writers who specialize in Federated Learning research and thesis development. We offer fast-track dissertation writing services to help you complete your Federated Learning thesis or dissertation smoothly and on time.

Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.

Federated Learning Research Paper Publishing Support

UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.

Project Synopsis and Presentation Support

We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Federated Learning final year project.

Federated Learning Research Support for PhD Scholars

UniPhD offers advanced Federated Learning research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.

Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.

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