Faculty Publications
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Item Towards a Federated Learning Approach for NLP Applications(Springer Science and Business Media Deutschland GmbH, 2021) Prabhu, O.S.; Gupta, P.K.; Shashank, P.; Chandrasekaran, K.; Divakarla, D.Traditional machine learning involves the collection of training data to a centralized location. This collected data is prone to misuse and data breach. Federated learning is a promising solution for reducing the possibility of misusing sensitive user data in machine learning systems. In recent years, there has been an increase in the adoption of federated learning in healthcare applications. On the other hand, personal data such as text messages and emails also contain highly sensitive data, typically used in natural language processing (NLP) applications. In this paper, we investigate the adoption of federated learning approach in the domain of NLP requiring sensitive data. For this purpose, we have developed a federated learning infrastructure that performs training on remote devices without the need to share data. We demonstrate the usability of this infrastructure for NLP by focusing on sentiment analysis. The results show that the federated learning approach trained a model with comparable test accuracy to the centralized approach. Therefore, federated learning is a viable alternative for developing NLP models to preserve the privacy of data. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item 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. © 2024 IEEE.Item 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. © 2024 IEEE.Item Integrating artificial intelligence in aquaculture: opportunities, risks, and systemic challenges(Springer Science and Business Media Deutschland GmbH, 2025) M R, D.; Sanshi, S.; Singh, M.P.; Gupta, M.Aquaculture plays a significant role in the food chain and in rural economies. Continuous water quality monitoring, health management, real-time growth monitoring, and biomass estimation are critical aquaculture activities. Recent advances in computer vision, image processing, and Artificial Intelligence (AI), particularly in Machine Learning (ML) and Deep Learning (DL), enable the control, drive, and solve the problems related to daily real-time aquaculture activities. The performance of various ML and DL models and the quality and availability of public datasets in this domain remain underexplored, and the redefinition of the role of AI in aquaculture is the motivation for this study. This survey aims to analyse and evaluate various methods and datasets for monitoring water quality, estimating fish biomass, disease prediction, and behavioural analysis, to highlight recent developments and to seek the attention of researchers to address the challenges and concerns of aquaculture systems. Currently, there is no single, generalised AI model capable of performing all essential aquaculture activities using continuous time-series data generated from diverse, heterogeneous ponds across a wide geographical area. To address this gap, a 5G-enabled Federated Learning (FL) framework utilising Unmanned Aerial Vehicles (UAV) for cooperative data collection and model training is highly recommended. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.Item 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.Item 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.Item 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.Item BENN: Balanced Ensemble Neural Network for Handling Class Imbalance in Big Data(John Wiley and Sons Inc, 2025) Sneha, S.H.; Annappa, B.; Pariserum Perumal, S.P.Class imbalance is a critical challenge in big data analytics, often leading to biased predictive models. This imbalance can lead to biased models that perform well on the majority class but poorly on the minority class. Many machine learning models tend to be biased towards the majority class because they aim to minimise overall error, often leading to poor performance on the minority class. This paper presents the balanced ensemble neural network, a novel solution to effectively address class imbalance in big data. Balanced ensemble neural network combines the robust capabilities of neural networks with the power of ensemble learning, incorporating class balancing strategies to ensure fair representation of minority classes. The methodology involves integrating multiple neural networks, each trained on balanced subsets of data using techniques like Synthetic Minority Over-sampling Technique and Random Undersampling. This integration aims to leverage the strengths of individual networks while reducing their inherent biases. Our extensive experiments across various datasets reveal that BENN achieves an AUC-ROC score of 0.94, surpassing other models such as random forest (0.88), support vector (0.84) and single neural net (0.80). It was also observed that BENN's performance is better compared to traditional neural network models and standard ensemble methods in key metrics like accuracy, precision, recall, F1-score and AUC-ROC. The results specifically highlight BENN's effectiveness in accurately classifying instances of minority classes, a notable challenge in many existing models. These findings underscore BENN's potential as a substantial advancement in handling class imbalance within big data environments, offering a promising direction for future research and application in machine learning. © 2024 John Wiley & Sons Ltd.Item 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.Item 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.