1. Abstract

Non-IID Data Handling in Federated Learning is a machine learning project designed to improve collaborative model training when data distributions differ across clients. In real-world applications, clients such as hospitals, mobile devices, and organizations often have different data characteristics, class distributions, and sample sizes. This variation, known as non-independent and identically distributed (Non-IID) data, can reduce model accuracy and slow down federated training. The proposed system uses Federated Learning techniques to train local models across distributed clients without directly sharing raw datasets. It applies strategies such as adaptive aggregation, client weighting, and local training optimization to reduce the impact of data heterogeneity. The server aggregates client model updates to create a global model, which is evaluated against conventional federated learning approaches. The project aims to improve model stability, convergence, and predictive performance across diverse client datasets.

2. Objectives

1. To develop a federated learning framework that handles Non-IID client data.
2. To simulate different data distributions across multiple clients.
3. To analyze the impact of data heterogeneity on model accuracy.
4. To implement strategies such as weighted aggregation and adaptive local training.
5. To improve global model convergence and training stability.
6. To compare FedAvg with selected Non-IID handling techniques.
7. To evaluate performance using accuracy, precision, recall, F1-score, and convergence time.
8. To visualize client-level and global model performance through a dashboard.

3. Existing System

Traditional Federated Learning algorithms, such as Federated Averaging (FedAvg), aggregate model updates from multiple clients to build a global model. These methods can work well when client data distributions are similar. However, real-world datasets often contain different class proportions, feature distributions, and sample sizes across clients. This data heterogeneity can cause inconsistent local model updates and slow global model convergence.

Disadvantages

1. Reduced accuracy when client datasets have different distributions.
2. Slower or unstable model convergence.
3. Biased global models when some clients dominate training.
4. Inconsistent performance across clients.
5. Difficulty learning minority classes.
6. Limited effectiveness of basic aggregation methods in highly heterogeneous environments.

4. Proposed System

The proposed system uses Federated Learning techniques specifically designed to handle Non-IID data. Each client trains a local model using its own dataset, while the server combines model updates using a selected aggregation strategy. The system can implement weighted aggregation, adaptive local training, and algorithms such as FedProx to reduce the negative effects of client data heterogeneity.

The global model is evaluated across clients with different data distributions and compared with the baseline FedAvg algorithm. The system aims to improve model robustness, convergence, and fairness across participating clients.

Key Features

1. Non-IID dataset partitioning across multiple clients.
2. Local model training without raw-data sharing.
3. FedAvg baseline implementation.
4. FedProx or other selected Non-IID handling strategies.
5. Weighted aggregation based on client data size.
6. Client-level and global performance evaluation.
7. Training convergence visualization.
8. Web-based dashboard for comparing algorithms.

5. Implementation Procedure

1. Dataset Collection: Select a suitable public dataset for classification, such as MNIST, Fashion-MNIST, or a healthcare prediction dataset.

2. Data Preprocessing: Clean the dataset, handle missing values where applicable, normalize features, and prepare training and testing sets.

3. Client Simulation: Divide the dataset into multiple client partitions representing separate users or organizations.

4. Non-IID Data Generation: Create heterogeneous client datasets using methods such as label-based partitioning, unequal sample sizes, or Dirichlet distribution-based partitioning.

5. Local Model Development: Build a machine learning model or neural network suitable for the selected dataset.

6. Baseline Federated Learning: Implement FedAvg and record its accuracy, convergence time, and client-level performance.

7. Non-IID Handling Algorithm: Implement FedProx or another selected approach to improve training with heterogeneous client data.

8. Model Aggregation: Aggregate local updates using the selected algorithm and distribute the global model to participating clients.

9. Iterative Training: Repeat local training and aggregation for multiple communication rounds.

10. Performance Evaluation: Compare algorithms using accuracy, precision, recall, F1-score, convergence speed, and performance variation across clients.

11. Dashboard Development: Build a Streamlit or Flask interface to visualize training curves, client distributions, and algorithm comparisons.

12. Testing: Evaluate the algorithms under different levels of data heterogeneity and client participation.

6. Software Requirements

7. Hardware Requirements

Recommended: Intel Core i5 or equivalent, 16 GB RAM, and SSD storage for experiments with multiple clients and neural network models.

8. Advantages of the Project

1. Improved Model Robustness: Addresses challenges caused by different client data distributions.
2. Better Training Stability: Selected optimization techniques can improve convergence in heterogeneous environments.
3. Collaborative Learning: Multiple clients contribute to a global model without directly sharing raw datasets.
4. Client-Level Evaluation: Reveals performance differences between individual clients.
5. Flexible Aggregation: Supports experimentation with FedAvg, FedProx, and other algorithms.
6. Reduced Data Movement: Keeps training data on individual clients.
7. Scalability: Supports experiments involving different client counts and data distributions.
8. Performance Comparison: Helps identify algorithms that work better under different levels of heterogeneity.