Browsing by Author "Sachin, D.N."

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A Multimodal Contrastive Federated Learning for Digital Healthcare
(Springer, 2023) Sachin, D.N.; Annappa, B.; Ambesenge, S.; Tony, A.E.
Digital healthcare applications have gained enormous global interest due to the rapid development of the internet of medical things (IoMT), which helps access massive amounts of multimodal healthcare data. Using this rich multimodal data without violating user privacy becomes crucial. Federated learning (FL) isolates data and protects user privacy. Clients collaboratively learn global models without data transmission. Most of the current FL approaches still depend on single-modal data. It is known that multimodal data always benefit from the complementarity of different modalities. This paper proposes a multimodal contrastive federated learning framework for digital healthcare. The proposed framework solves the multimodal federated learning problem. The proposed architecture used a geometric multimodal contrastive representation learning method to learn representations of multiple modalities in a shared, high-dimensional space. This helps optimize the representations to capture the inter-modal relationships better and improves the multimodal model’s overall performance. Experiments show that the proposed framework performs better than conventional single-modality FL and multimodal FL framework approaches. Given its generality and extensibility, the proposed framework can be used for many downstream tasks in healthcare applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Abdominal Multi-Organ Segmentation Using Federated Learning
(Institute of Electrical and Electronics Engineers Inc., 2024) Yadav, G.; Annappa, B.; Sachin, D.N.
Multi-organ segmentation refers to precisely de-lineating and identifying multiple organs or structures within medical images, such as Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI), to outline boundaries and regions for each organ accurately. Medical imaging is crucial to comprehending and diagnosing a wide range of illnesses for which accurate multi-organ image segmentation is often required for successful analysis. Due to the delicate nature of medical data, traditional methods for multi-organ segmentation include centralizing data, which presents serious privacy problems. This centralized training strategy impedes innovation and collaborative efforts in healthcare by raising worries about patient confidentiality, data security, and reg-ulatory compliance. The development of deep learning-based image segmentation algorithms has been hindered by the lack of fully annotated datasets, and this issue is exacerbated in multi-organ segmentation. Federated Learning (FL) addresses privacy concerns in multi-organ segmentation by enabling model training across decentralized institutions without sharing raw data. Our proposed FL-based model for CT scans ensures data privacy while achieving accurate multi-organ segmentation. By leveraging FL techniques, this paper collaboratively trains segmentation models on local datasets held by distinct medical institutions. The expected outcomes encompass achieving high Dice Similarity Coefficient (DSC) metrics and validating the efficacy of the proposed FL approach in attaining precise and accurate segmentation across diverse medical imaging datasets. Â© 2024 IEEE.
ARIMA-PID: container auto scaling based on predictive analysis and control theory
(Springer, 2024) Joshi, N.S.; Raghuwanshi, R.; Agarwal, Y.M.; Annappa, B.; Sachin, D.N.
Containerization has become a widely popular virtualization mechanism alongside Virtual Machines (VMs) to deploy applications and services in the cloud. Containers form the backbone of the modern architectures around microservices and provide a lightweight virtualization mechanism for IoT and Edge systems. Elasticity is one of the key requirements of modern applications with various constraints ranging from Service Level Agreements (SLA) to optimization of resource utilization, cost management, etc. Auto Scaling is a technique used to attain elasticity by scaling the number of containers or resources. This work introduces a novel mechanism for auto-scaling containers in cloud environments, addressing the key elasticity requirement in modern applications. The proposed mechanism combines predictive analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model and control theory utilizing the Proportional-Integral-Derivative (PID) controller. The major contributions of this work include the development of the ARIMA-PID algorithm for forecasting resource utilization and maintaining desired levels, comparing ARIMA-PID with existing threshold mechanisms, and demonstrating its superior performance in terms of CPU utilization and average response times. Experimental results showcase improvements of approximately 10% in CPU utilization and 30%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
EdgeFedNet: Edge Server Based Communication and Computation Efficient Federated Learning
(Springer, 2025) Gowtham, L.; Annappa, B.; Sachin, D.N.
Federated learning (FL) is a new learning framework for training machine learning and deep learning models using data spread over several edge devices. Edge devices like mobile phones and IoT devices have constraints on computational power, resources, and connectivity for training the model. Also, many model parameters will be exchanged while training the model, leading to high communication costs in FL when bandwidth is limited. This paper presents EdgeFedNet a new form of training the model in FL. The proposed method reduces the model parameters by pruning the model and restricts the communication between clients and the cloud server by implementing edge servers. An edge server near a set of clients forms a cluster and coordinates the FL training. The aggregated model updates from all the edge servers are sent to the cloud server, restricting the frequent communication between the clients and the cloud server. The experimental results exhibit a remarkable reduction in the number model parameters (up to 54%) and effectively address the communication overhead by reducing communication rounds by 59% compared to the baseline approach FedAvg. These enhancements are achieved without sacrificing accuracy, presenting promising implications for more efficient model parameter pruning and communication strategies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Enhancing Healthcare AI with Cross-Silo Personalized Federated Learning on Naturally Split Heterogeneous Data
(Institute of Electrical and Electronics Engineers Inc., 2024) Mukeshbhai, A.N.; Annappa, B.; Sachin, D.N.
The potential of Artificial Intelligence (AI) in health-care is unavoidable. However, its success depends on the availabil-ity of large, high-quality datasets. Because of data heterogeneity across institutions and privacy concerns, traditional centralized Machine Learning (ML) approaches often face difficulties in this field. Federated Learning (FL) allows collaborative model training without requiring the transfer of sensitive patient data from the original institution. Recent research in FL within the healthcare domain has predominantly relied on centralized datasets, which do not represent real-time data heterogeneity and made assumptions by random data splitting to different medical client institutions. Additionally, it may be challenging for a single global model to encompass the diverse characteristics of various healthcare settings accurately. This paper examines the application of Personalized Federated Learning (PFL) in realistic cross-silo healthcare scenarios with federated natural split datasets in different medical client institutions. This paper discusses the experiments conducted on brain segmentation, survival prediction, melanoma classification, and heart disease di-agnosis. Our experiments show that the proposed PFL techniques consistently improve local model performance over standard FL strategies by up to 10% in different medical use cases. Â© 2024 IEEE.
FedCure: A Heterogeneity-Aware Personalized Federated Learning Framework for Intelligent Healthcare Applications in IoMT Environments
(Institute of Electrical and Electronics Engineers Inc., 2024) Sachin, D.N.; Annappa, B.; Hegde, S.; Abhijit, C.S.; Ambesange, S.
The advent of the Internet of Medical Things (IoMT) devices has led to a healthcare revolution, introducing a new era of smart applications driven by Artificial Intelligence (AI). These advanced technologies have greatly influenced the healthcare industry and have played a crucial role in enhancing the quality of life globally. Federated Learning (FL) has become popular as a technique to create models that can be shared universally using the vast datasets collected from IoMT devices while maintaining data privacy. However, the complex variations in IoMT environments, including diverse devices, data characteristics, and model complexities, create challenges for the straightforward application of traditional FL methods. Consequently, it is not well-suited for deployment in such contexts. This paper introduces FedCure, a personalized FL framework tailored for intelligent IoMT-based healthcare applications operating within a cloud-edge architecture. FedCure is adept at addressing the challenges within IoMT environments by employing personalized FL techniques that can effectively mitigate the impact of heterogeneity. Furthermore, the integration of edge computing technology enhances processing speed and minimizes latency in intelligent IoMT applications. Lastly, this research showcases several case studies encompassing IoMT-based applications, such as Eye Retinopathy Detection, Diabetes Monitoring, Maternal Health, Remote Health Monitoring, and Human Activity Recognition. These case studies provide a means to assess the effectiveness of the proposed FedCure framework and showcase exceptional performance with accuracy and minimal communication overhead, especially in addressing the challenges posed by heterogeneity. © 2013 IEEE.
Federated learning for digital healthcare: concepts, applications, frameworks, and challenges
(Springer, 2024) Sachin, D.N.; Annappa, B.; Ambesange, S.
Various hospitals have adopted digital technologies in the healthcare sector for various healthcare-related applications. Due to the effect of the Covid-19 pandemic, digital transformation has taken place in many domains, especially in the healthcare domain; it has streamlined various healthcare activities. With the advancement in technology concept of telemedicine evolved over the years and led to personalized healthcare and drug discovery. The use of machine learning (ML) technique in healthcare enables healthcare professionals to make a more accurate and early diagnosis. Training these ML models requires a massive amount of data, including patients’ personal data, that need to be protected from unethical use. Sharing these data to train ML models may violate data privacy. A distributed ML paradigm called federated learning (FL) has allowed different medical research institutions, hospitals, and healthcare devices to train ML models without sharing raw data. This survey paper overviews existing research work on FL-related use cases and applications. This paper also discusses the state-of-the-art tools and techniques available for FL research, current shortcomings, and future challenges in using FL in healthcare. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024.
Federated Learning forÂ 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 learningâ€™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%. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Federated Split Learning with HyperNetworks for Medical Image Classification
(Institute of Electrical and Electronics Engineers Inc., 2024) Sindhura, S.; Annappa, B.; Sachin, D.N.
The integration of Artificial Intelligence (AI) in the medical field has significantly enhanced patient healthcare by enabling the development of sophisticated predictive models. However, the conventional method of building these AI models often requires the aggregation of vast amounts of centralized data, which poses substantial risks to patient data privacy and presents major barriers to scaling these solutions. Federated Learning (FL), a distributed machine learning paradigm, addresses this issue by allowing multiple clients to collaboratively train a model without exchanging their data, thus preserving privacy. Despite its benefits, FL faces significant data and computational heterogeneity challenges. Medical environments frequently feature devices with varied computational capacities, which can severely impede the performance of FL. This paper presents a novel model partitioning approach within the Split Learning (SL) framework by incorporating Dynamic Hypernetworks, which dynamically adjust network parameters in real-time. This method optimizes theâ€™cut layerâ€™â€”the point at which data is split between client and serverâ€”thereby enhancing computational efficiency and significantly reducing the risk of sensitive information leakage. It enables efficient model training on resource-constrained devices and demonstrates superior performance in a medical classification task when compared to traditional centralized, FL, and standard SL methods. The results show improved effectiveness in maintaining data privacy and highlight the potential of this approach in facilitating the broader adoption of AI in healthcare. Â©2024 IEEE.
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. Â© 2024 IEEE.
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. Â© 2022 IEEE.
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. Â© 2023 IEEE.
Smart client selection strategies for enhanced federated learning in digital healthcare applications
(Springer, 2025) Sachin, D.N.; Annappa, B.; Ambesange, S.
Federated Learning (FL) trains AI models in healthcare without sharing patient data. FL computes client models locally and combines them to create a global model. However, involving all clients is impractical due to resource limitations. Random selection of a subset of clients in each FL round can pose challenges for resource-limited devices, leading to longer processing times and potential training failures. To tackle these obstacles, this research proposes a novel strategy for FL that treats each training round as a client selection process to improve the efficiency and effectiveness of FL in healthcare applications, where data privacy is paramount. The approach begins by calculating the uncertainty value for each client, which quantifies the contribution of the client’s data to the overall model. Clients are then ranked based on their uncertainty values, and those with higher loss values are given a higher probability of participating in the training process. The experimental outcomes clearly show that the proposed strategy effectively makes 1.3x training faster, and 30% lowers communication expenses, conserves computational resources, and enhances model performance when contrasted with random client selection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.