Conference Papers
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Item Evaluation of Machine Learning approaches for resource constrained IIoT devices(Institute of Electrical and Electronics Engineers Inc., 2021) Akubathini, P.; Chouksey, S.; Satheesh, H.S.Resource-constrained devices such as sensors, industrial controllers, analyzers etc., mostly contain limited computational capacity and memory. They are largely deployed in all industries and have been generating a huge amount of data. This data is sent to the cloud servers where various Machine Learning (ML) algorithms are applied to perform the analysis or prediction as per the application. In this process, communication requires bandwidth and time. Since the data is sent into the network, the privacy of the data is not guaranteed. Cloud servers consume a huge amount of power. To reduce these cost factors, the machine learning models are compressed and optimized such that they can fit and run in small footprint devices. The Federated Learning (FL) approach at the edge device level promises to address the data privacy and bandwidth related issues. Since it is a decentralized learning method across a set of devices, the performance of the model also improves. This paper describes and evaluates the machine learning algorithms with various compression methods suitable for resource-constrained IIoT devices and federated learning approach, particularly for time series data applications. Simulation results show that FastGRNN algorithm gives the least model size compared to the traditional RNN algorithms for time series. © 2021 IEEE.Item FedPruNet: Federated Learning Using Pruning Neural Network(Institute of Electrical and Electronics Engineers Inc., 2022) Gowtham, L.; Annappa, A.; Sachin, D.N.Federated Learning (FL) is a distributed form of training the machine learning and deep learning models on the data spread over heterogeneous edge devices. The global model at the server learns by aggregating local models sent by the edge devices, maintaining data privacy, and lowering communication costs by just communicating model updates. The edge devices on which the model gets trained usually will have limitations towards power resource, storage, computations to train the model. This paper address the computation overhead issue on the edge devices by presenting a new method named FedPruNet, which trains the model in edge devices using the neural network model pruning method. The proposed method successfully reduced the computation overhead on edge devices by pruning the model. Experimental results show that for the fixed number of communication rounds, the model parameters are pruned up to 41.35% and 65% on MNIST and CIFAR-10 datasets, respectively, without compromising the accuracy compared to training FL edge devices without pruning. © 2022 IEEE.Item Simulating Federated Transfer Learning for Lung Segmentation using Modified UNet Model(Elsevier B.V., 2022) Ambesange, S.; Annappa, A.; Koolagudi, S.G.Lung segmentation helps doctors in analyzing and diagnosing lung diseases effectively. Covid -19 pandemic highlighted the need for such artificial intelligence (AI) model to segment Lung X-ray images and diagnose patient covid conditions, in a short time, which was not possible due to huge number of patient influx at hospitals with the limited radiologist to diagnose based on test report in short time. AI models developed to assist doctors to diagnose faster, faces another challenge of data privacy. Such AI Models, for better performance, need huge data collected from multiple hospitals/diagnostic centres across the globe into single place to train the AI models. Federated Learning (FL) framework, using transfer learning approach addresses these concerns as FL framework doesn't need data to be shared to outside hospital ecosystem, as AI model get trained on local system and AI model get trained on distributed data. FL with Transfer learning doesn't need the parallel training of the model at all participants nodes like other FL. Paper simulates Federated Transfer learning for Image segmentation using transfer learning technique with few participating nodes and each nodes having different size dataset. The proposed method also leverages other healthcare data available at local system to train the proposed model to overcome lack of more data. Paper uses pre-trained weights of U-net Segmentation Model trained for MRI image segmentation to lung segmentation model. Paper demonstrates using such similar healthcare data available at local system helps improving the performance of the model. The paper uses Explainable AI approach to explain the result. Using above three techniques, Lung segmentation AI model gets near perfect segmentation accuracy. © 2023 The Authors. Published by Elsevier B.V.Item Federated Learning for Wearable Sensor-Based Human Activity Recognition(Springer Science and Business Media Deutschland GmbH, 2023) Sachin, D.N.; Annappa, B.; Ambesenge, S.Computing devices that can identify various human behaviors or motions may be used to aid individuals in multiple contexts, including sports, healthcare, and interactions between humans and robots. Data readily accessible for this purpose may be collected from smartphones and wearable devices used daily. Therefore, efforts are made to classify real-time activity data effectively utilizing various machine learning models. However, current methods for human activity recognition do not sufficiently consider user data privacy. To mitigate privacy issues, federated learning can be employed to build generic activity classification model by aggregating a locally trained model at a user-edge device. This paper adopted a deep-learning neural network model called the transformer for motion signal time-series analysis. It uses the attention mechanism to provide context for each point in the time series. It also compares federated learning’s performance to centralized learning. Experimental results show that federated learning outperformed centralized training without com- promising user data privacy, with an accuracy of 96.87%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Trusted Federated Learning Framework for Attack Detection in Edge Industrial Internet of Things(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Anand, A.; Prateek Janaswamy, L.A.; Sundarrajan, S.; Gupta, M.The edge Industrial Internet of Things (IIoT) is highly vulnerable to attacks due to the vast number of connected devices and the lack of security features. Attacks in edge IIoT can lead to significant damage, including data theft, malfunctioning, and privacy breaches. Federated Learning (FL) is a promising approach to detecting attacks by utilizing edge devices’ collective intelligence. FL allows devices to collaboratively learn from multiple devices’ data without centralized sharing, which preserves data privacy and reduces communication costs. However, FL has vulnerabilities that can compromise model accuracy, privacy, and security. Trusted FL is essential for collaboration among multiple edge IIoT devices while preserving data privacy and security. Trust plays a critical role in the success of FL, as edge IIoT devices must trust that the models are accurately learning and that their data is protected. To address this, we propose an FL framework that uses Federated Averaging (FedAvg) and Convolutional Neural Network (CNN) to detect attacks in edge IIoT. We also propose a mechanism to calculate trust for appropriate edge IIoT device selection by measuring each device’s (a.k.a client’s) performance during model training. The proposed edge IIoT device selection method, client selection, can fairly select clients for model training and improve trust in the entire system. Although the proposed FL approach does not outperform the centralized ResNet-18 CNN model on experimental analysis, improving its performance can be a promising solution for detecting attacks in edge IIoT. © 2023 IEEE.Item FedRH: Federated Learning Based Remote Healthcare(Institute of Electrical and Electronics Engineers Inc., 2023) Sachin, D.N.; Annappa, B.; Ambesenge, S.More and more people are developing chronic illnesses, which require regular visits to the hospital for treatment and monitoring. Due to the advancements in wearable computing technology, there is a growing interest in remote health monitoring worldwide. The Internet of Medical Things (IoMT) devices allow users to access their health data. Recent developments in smart healthcare have shown that machine learning model training using vast amounts of healthcare data has been remarkably successful. However, remote healthcare is facing a significant challenge as user data is stored in isolated silos, thus making data aggregation impractical while ensuring data privacy and security. To overcome this challenge, this paper presents FedRH, a federated learning framework for remote healthcare. FedRH solves the issue of data isolation through a cloud-edge-based computing architecture and FL, providing personalized healthcare without sacrificing privacy and security. Experiments show that FedRH achieves a greater accuracy of 5.6% compared to conventional approaches. FedRH is a flexible and versatile tool that can be applied to various healthcare applications, making it an ideal choice for many use cases. © 2023 IEEE.Item FedLSF: Federated Local Graph Learning via Specformers(Institute of Electrical and Electronics Engineers Inc., 2024) Ram Samarth, B.B.; Annappa, B.; Sachin, D.N.The abundance of graphical data and associated privacy concerns in real-world scenarios highlight the need for a secure and distributed methodology utilizing Federated Learning for Graph Neural Networks(GNNs). While spatial GNNs have been explored in FL, spectral GNNs, which capture rich spectral information, remain relatively unexplored. Despite enhancing GNNs' expressiveness through attention-based mechanisms, challenges persist in the spatial approach for FL due to cross-client edges. This work introduces two information capture methods for spectral GNNs in FL settings, Global Information Capture and Local Information Capture, which address cross-client edges. Federated Local Specformer (FedLSF) is proposed as a novel methodology that combines local information capture with state-of-the-art(SOTA) Specformer, enabling local graph learning on clients. FedLSF leverages Specformers involving spectral and attention approaches by integrating Eigen Encoding, Transformer architecture, and graph convolution. This enables capturing rich information from eigen spectra and addresses concerns related to cross-client edges through fully connected eigen-spaces. Experimental results demonstrate FedLSF's efficacy in both homophily and heterophily datasets, showing significant accuracy improvements (2-50)% in highly non-independent and identically distributed (Non-IID) scenarios compared to the present SOTA. This research advances attention-based spectral mechanisms in FL for GNNs, providing a promising solution for preserving privacy in non-IID graph data environments. Implementation can be found at https://github.com/achiverram28/FedLSF-DCOSS. © 2024 IEEE.Item FL-DABE-BC: A Privacy-Enhanced Decentralized Authentication and Secure Communication Framework for FL in IoT-Enabled Smart Cities(Association for Computing Machinery, Inc, 2025) Narkedimilli, S.; Pravisha, P.; Sriram, A.V.; Raghav, S.; Vangapandu, P.Federated Learning (FL) offers a distributed approach to machine learning that preserves data privacy by avoiding the exchange of sensitive IoT sensor information. This paper introduces a novel IoT framework that integrates advanced security tools to tackle key privacy and security challenges. It employs Decentralized Attribute-Based Encryption (DABE) for decentralized authentication and data encryption, Homomorphic Encryption (HE) for secure computations on encrypted data, Secure Multi-Party Computation (SMPC) for collaborative processing, and Blockchain for distributed ledger management and transparent communication. In this system, IoT devices encrypt data locally with DABE, while initial model training occurs on cloud servers within an immutable blockchain network that supports peer-to-peer authentication. Encrypted model weights are then transferred to the fog layer via HE and aggregated using SMPC, after which the FL server updates and distributes the global model to the IoT devices. This innovative framework effectively addresses the challenges of secure decentralized learning, enabling privacy-preserving, efficient, and secure federated learning for IoT applications and real-time analytics. © 2025 Copyright held by the owner/author(s).