• Federated Learning (FL) is an emerging machine learning (ML) paradigm that enables decentralized data processing by training models collaboratively across multiple devices or locations, without the need to share raw data. Unlike traditional machine learning approaches where data is centralized on a single server for training, Federated Learning allows devices (e.g., smartphones, edge devices, or IoT devices) to retain their data locally, improving privacy, data security, and scalability. Only the model updates (i.e., gradients or parameters) are shared with a central server, which aggregates these updates to create a global model.
  
  This approach is particularly useful in domains where privacy is critical, such as healthcare, finance, and mobile applications, where sensitive user data cannot be shared or centralized due to regulatory or privacy concerns. Python is an ideal language for implementing Federated Learning systems due to its rich ecosystem of libraries for machine learning, communication protocols, and data privacy.
  
  Final-year projects in Federated Learning offer students a chance to explore cutting-edge solutions that address the challenges of distributed learning, such as communication efficiency, data heterogeneity, privacy preservation, and model robustness.

Software Tools and Technologies

• • Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
• • Development Tools: Anaconda3 / Spyder 5.0 / Jupyter Notebook
• • Language Version: Python 3.11.1
• • Python ML Libraries: Scikit-Learn / Numpy / Pandas / Matplotlib / Seaborn.
• • Deep Learning Frameworks: Keras / TensorFlow / PyTorch.

List Of Final Year Python Projects in Federated Learning