Intelligent Federated Learning Framework for Non-colocated and Heterogeneous Datasets

Summary

This protocol describes the implementation of an Intelligent Federated Learning Framework for training distributed machine learning models across heterogeneous, non-colocated datasets while preserving data privacy and enabling model interpretability.

Abstract

Federated learning has significant potential for distributed model training while preserving privacy, but it faces challenges related to convergence, fairness, and interpretability due to the heterogeneity of non-colocated datasets. This study proposes an Intelligent Federated Learning Framework (IFLF) to address these challenges through an adaptive approach using a multi-layer architecture consisting of Data, Client, Aggregation, Adaptation, and Optimization, and Interpretability layers.

The framework demonstrates stable convergence under non-IID data distributions, with aggregation strategies supporting balanced optimization. The learning-rate modulation approach contributes to stable training by integrating heterogeneous client updates and reducing divergence during optimization. Explainable AI techniques, including SHAP and LIME, are incorporated to improve transparency at both client and global levels.

The IFLF is evaluated on four benchmark datasets (FEMNIST (vision), FLamby (healthcare imaging), FedGraphNN (graph learning), and CICIDS2017 (cybersecurity)). The framework achieved an average accuracy of 92.8%, with faster convergence and reduced performance variability across clients.

Introduction

The rapid increase in distributed data originating from mobile devices, healthcare systems, industrial sensors, and cyber-physical infrastructures has increased the need for collaborative machine learning techniques that also preserve data privacy. Conventional machine learning methods rely on centralized data consolidation, in which datasets from different sources are gathered and stored in a single repository. Although such an approach facilitates model training, it raises concerns related to data privacy, regulatory compliance, and communication overhead. Federated learning (FL) has been recognized as a promising approach that enables multiple clients to jointly tr....

Protocol

Overview
An Intelligent Federated Learning Framework (IFLF) was developed to efficiently manage data and system heterogeneity in non-colocated environments. The system architecture consisted of five layers: the Data Layer, Client Layer, Aggregation Layer, Adaptation and Optimization Layer, and the Interpretability Layer. The modules were deployed over a distributed computing setup with a central aggregation server and multiple client nodes. Secure socket connections (SSL/TLS) were used for communication between nodes to ensure privacy and data integrity. The following procedure was used to prepare datasets, set up the architecture, conduct federat....

Results

Overview
All experimental results reported in this study were averaged over five independent runs with different random initializations and client sampling configurations to ensure robustness. Performance metrics, including accuracy, precision, recall, and F1-score, are presented as mean ± standard deviation. The observed standard deviation across runs remained within a small range (typically ±0.5% to ±1.2%), indicating stable and consistent performance of the proposed framework under heterogeneous f.......

Discussion

The proposed Intelligent Federated Learning Framework (IFLF) addresses challenges in federated learning arising from heterogeneous and distributed environments. Systematic evaluation across four datasets indicates that combining aggregation and adaptive optimization improves convergence stability and global model performance. By incorporating client reliability and similarity into the aggregation process, the framework reduces the influence of inconsistent updates while giving greater weight to more reliable client contr.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CICIDS2017 Dataset	Canadian Institute for Cybersecurity	Public dataset	Intrusion detection
FedGraphNN Dataset	FedGraphNN	Public dataset	Graph learning
FEMNIST Dataset	LEAF Benchmark	Public dataset	Vision dataset
FLamby Dataset	FLamby Project	Public dataset	Healthcare dataset
Flower	Flower Framework	1.6	Federated learning simulation
GPU Workstation	NVIDIA	RTX GPU	Model training
NumPy	NumPy Developers	1.26	Numerical computing
Python	Python Software Foundation	3.1	Programming language
Scikit-learn	Scikit-learn	1.3	ML utilities
TensorFlow / PyTorch	Google / Meta	Latest	Deep learning framework