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Federated learning with Google FL

Why Choose This Project?

With growing privacy concerns, transmitting raw data to a central server is often not feasible. Federated Learning (FL) enables multiple devices or organizations to collaboratively train a machine learning model without sharing their raw data. Only model updates are exchanged, preserving privacy.

This project is ideal for applications like healthcare (patient data), finance (sensitive transactions), mobile predictive keyboards, IoT devices, and any scenario requiring privacy-preserving ML.

What You Get

Federated training of a machine learning model across multiple clients/devices
Central server aggregates model updates without accessing raw data
Evaluation of global model accuracy and convergence
Privacy-preserving workflows with secure aggregation
Optional integration with TensorFlow Federated (TFF) or PySyft

Key Features

Feature	Description
Local Model Training	Each client/device trains a local model on its own data
Model Aggregation	Central server collects gradients/weights and updates global model
Privacy-Preserving	Raw data never leaves the client device
Support for Multiple ML Tasks	Classification, regression, NLP tasks on distributed data
Cross-Device & Cross-Silo FL	Train across mobile devices (cross-device) or organizations (cross-silo)
Metrics & Evaluation	Evaluate model locally and globally after aggregation
Scalable Architecture	Add more clients without changing the server logic
Integration with Google FL	Use TensorFlow Federated (TFF) or FL APIs for training

Technology Stack

Layer	Tools/Technologies
ML Framework	TensorFlow Federated (TFF), PyTorch + PySyft
Backend Server	Python (Flask/Django) for aggregation server
Frontend / Client	Python scripts or lightweight apps for training on client data
Data Storage	Local client storage (CSV, SQLite, JSON)
Communication	gRPC / HTTP / WebSockets for model updates
Monitoring	TensorBoard for model metrics, server logs

Google Cloud Services Used

Google Cloud Service	Purpose
AI Platform	Deploy ML models and manage training
Cloud Functions	Optional serverless aggregation
Cloud Pub/Sub	Messaging between clients and server
Cloud Storage	Optional model checkpoint storage
BigQuery	Optional analytics on aggregated metrics
Vertex AI	Optional for model deployment and monitoring

Working Flow

Client Initialization
Each client loads its local dataset and initializes a model.
Local Training
Clients train their models locally for a few epochs.
Model Update Upload
Clients send only model weights or gradients to the central server.
Aggregation
Server aggregates updates (e.g., via Federated Averaging) and updates the global model.
Global Model Distribution
Updated global model is sent back to clients for the next training round.
Iteration
Steps 2–5 repeat until model converges.
Evaluation
Model performance is evaluated on client-held test data without accessing raw datasets.