Computer Engineering, Artificial Intelligence, Human-Computer Interaction, Energy
42
Scopus Publications
725
Scholar Citations
14
Scholar h-index
17
Scholar i10-index
Scopus Publications
Federated continual learning meets digital twins: A survey on methods, intersections and perspectives Martina Savoia, Daniela Annunziata, Dipanwita Thakur, Giancarlo Fortino, Francesco Piccialli Neurocomputing, 2026 Federated Learning (FL), Continual Learning (CL), and Digital Twins (DTs) have emerged as key paradigms for the development of intelligent, adaptive, and privacy-aware systems in various domains. FL enables collaborative model training across decentralized data sources without sharing raw data, thus ensuring privacy. CL allows models to continuously learn from evolving data streams and adapt to dynamic environments, reducing the need for retraining from scratch. DTs provide accurate virtual representations of physical systems, supporting real-time monitoring, simulation, and predictive maintenance. Combining these paradigms is a recent strategy for building physical systems that are decentralized, adaptive, and continuously improve using real-time data in various contexts. For example, in industries the integration of FCL with DTs can enable factories to learn from new sensor data across distributed sites while preserving sensitive data, adapting to equipment changes, and optimizing maintenance cycles. In mobile edge computing, this combination can enhance service reliability and user experience by updating models based on fresh data and dynamic user behavior. However, their combination also amplifies the inherent challenges, such as model drift, system complexity, and resource constraints, that need to be managed. Despite its promising potential, no existing surveys offer a focused and structured analysis of their intersection. This survey presents the first structured and comprehensive analysis of these three paradigms, highlighting not only existing approaches but also discussing their potential synergies and conflicts, outlining open research questions that must be addressed to unlock their full potential in real-world applications. • Identifies key challenges and outlines future research directions in FCL+DT. • Taxonomy and analysis of FCL methods applied to real-world DT systems. • First survey on the convergence of FCL and Digital Twin technologies.
Adaptive Sensitivity-Aware Differential Privacy Accounting for Federated Smart-Meter Theft Detection Diego Labate, Dipanwita Thakur, Giancarlo Fortino Big Data and Cognitive Computing, 2026 Smart-meter theft detection requires learning from fine-grained electricity consumption data, whose centralized processing poses significant privacy risks. Federated learning (FL) mitigates these risks by decentralizing training, but providing rigorous user-level differential privacy (DP) under non-IID data and heterogeneous client behavior remains challenging. Existing DP-FL approaches rely on fixed global clipping bounds for client updates, which substantially overestimate sensitivity when privacy loss is composed using Rényi Differential Privacy (RDP), zero-Concentrated DP (zCDP), or Moments Accountant (MA) frameworks, leading to excessive noise and degraded utility. This work proposes an adaptive clipping-based RDP accountant that incorporates empirical, round-wise update magnitudes into privacy accounting by rescaling each round’s RDP contribution according to the observed clipping ratio. The method is optimizer-agnostic and is evaluated with FedAvg, FedProx, and SCAFFOLD on the SGCC smart-meter theft dataset under IID and Dirichlet non-IID partitions. Experimental results show consistently tighter privacy bounds and improved model utility compared to classical DP accountants, demonstrating the effectiveness of sensitivity-aware privacy accounting for practical differentially private FL.
Empirical Analysis of FedAvg, FedProx and SCAFFOLD for Heterogeneous Data Distributions Farwa Ikram, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino 2026 1st International Conference on Innovations in Information and Communication Technologies Iict 2026, 2026 With the rise of big data and AI advancements, Federated Learning (FL) has gained significant research interest. FL operates on decentralized, often heterogeneous (non-IID) data, posing challenges in distributed databases. While various FL algorithms address non-IID issues, few comprehensively evaluate their impact across different non-IID distributions or propose effective improvements. This paper empirically analyzes FedAvg, FedProx, and SCAFFOLD using two datasets and two non-IID data partitioning schemes-Dirichlet-based and classbased. Our study highlights practical bottlenecks and theoretical insights, concluding with future research directions to enhance FL algorithm performance.
Agentic ElderFedLearn: A Differential Privacy-Based Approach for Elderly Disease Prediction Sunder Ali Khowaja, Kapal Dev, Dipanwita Thakur, Giancarlo Fortino IEEE Transactions on Computational Social Systems, 2026 Alzheimer’s disease (AD) is considered to be a significant health challenge that affects the cognitive ability of elderly people. The effects can only be slowed down if the disease is detected at an early stage. Researchers have extensively explored the use of machine learning algorithms to ensure early detection and prediction. However, effective models are complex, hence limiting their interpretability and privacy. Federated learning (FL) approaches have also been proposed to add privacy aspect to the machine learning models, however, FL methods are vulnerable to model related attacks. To address this we propose Agentic ElderFedLearn, a novel framework that proceeds in the following steps: 1) model healthcare institutions as autonomous artificial intelligence (AI) agents training local models on multimodal data [electronic health record (EHR) and synthetic magnetic resonance imaging (MRI)]; 2) apply personalized differential privacy (DP) to gradients, adapting budgets based on dataset size and sensitivity; 3) use multiagent reinforcement learning (MARL) to optimize agent interactions, such as privacy adjustments and communication; and 4) perform effective aggregation via weighted trimmed mean to defend against attacks. This innovation ensures privacy, handles heterogeneity, and achieves 94% accuracy with 0.93 F1-score, outperforming centralized approaches while using synthetic data.
Green Federated Learning: A New Era of Green Aware AI Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino, Francesco Piccialli ACM Computing Surveys, 2025 The development of AI applications, especially in large-scale wireless networks, is growing exponentially, alongside the size and complexity of the architectures used. Particularly, machine learning is acknowledged as one of today’s most energy-intensive computational applications, posing a significant challenge to the environmental sustainability of next-generation intelligent systems. Achieving environmental sustainability entails ensuring that every AI algorithm is designed with sustainability in mind, integrating green considerations from the architectural phase onwards. Recently, Federated Learning (FL), with its distributed nature, presents new opportunities to address this need. Hence, it is imperative to elucidate the potential and challenges stemming from recent FL advancements and their implications for sustainability. Moreover, it is crucial to furnish researchers, stakeholders, and interested parties with a roadmap to navigate and understand existing efforts and gaps in green-aware AI algorithms. This survey primarily aims to achieve this objective by identifying and analyzing over a hundred FL works and assessing their contributions to green-aware artificial intelligence for sustainable environments, with a specific focus on IoT research. It delves into current issues in green federated learning from an energy-efficient standpoint, discussing potential challenges and future prospects for green IoT application research.
Preface to the Proceedings of Green-Aware AI 2024 Ceur Workshop Proceedings, 2025
EAPD-CS: Energy Aware Performance Driven Client Selection in Federated Learning based Human Activity Recognition Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino Conference Proceedings IEEE International Conference on Systems Man and Cybernetics, 2025 Human Activity Recognition (HAR) represents a significant domain within pervasive computing, facilitating a diverse array of applications ranging from healthcare to smart environments. Traditional HAR models suffer from several challenges, including data privacy and the distributed participation of heterogeneous resource-constrained devices. To mitigate these challenges, the research community popularly uses federated learning (FL). However, selecting clients in FL is a critical issue, mainly when there is a combination of resource-constrained heterogeneous devices. This paper proposes a resource-and performance-aware client selection algorithm for HAR, namely EAPD-CS, that amalgamates the benefits of FL with energy efficiency. The framework allows for the training of machine learning models across multiple devices without the necessity of sharing raw data, thereby preserving user privacy. Additionally, it employs energy-aware strategies to diminish the carbon footprint and reduce the computational costs typically linked to traditional cloud-based HAR systems. Experimental results indicate that the proposed framework achieves more than 90% accuracy, comparable to centralized models, while significantly lowering energy consumption and improving the robustness of the model. This work contributes to the evolving field of green AI, delivering an effective, privacy-preserving, and environmentally sustainable approach for HAR applications.
Energy Aware Federated Learning with Application of Activity Recognition Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino 2023 IEEE International Conference on Dependable Autonomic and Secure Computing International Conference on Pervasive Intelligence and Computing International Conference on Cloud and Big Data Computing International Conference on Cyber Science and Technology Congress Dasc Picom Cbdcom Cyberscitech 2023, 2023
Adaptive Sensitivity-Aware Differential Privacy Accounting for Federated Smart-Meter Theft Detection D Labate, D Thakur, G Fortino Big Data and Cognitive Computing 10 (4), 113 , 2026 2026
Federated continual learning meets digital twins: A survey on methods, intersections and perspectives M Savoia, D Annunziata, D Thakur, G Fortino, F Piccialli Neurocomputing, 133366 , 2026 2026 Citations: 3
Agentic ElderFedLearn: A Differential Privacy-Based Approach for Elderly Disease Prediction SA Khowaja, K Dev, D Thakur, G Fortino IEEE Transactions on Computational Social Systems , 2026 2026
Layer-wise Quantization in Green-Aware AI F Ikram, D Thakur, A Guzzo, G Fortino 2nd Workshop on Green-Aware Artificial Intelligence, 28th European … , 2026 2026 Citations: 1
Empirical Analysis of FedAvg, FedProx and SCAFFOLD for Heterogeneous Data Distributions F Ikram, D Thakur, A Guzzo, G Fortino 2026 1st International Conference on Innovations in Information and … , 2026 2026
Exploring Process Mining in Human Activity Recognition: Challenges and Future Directions D Thakur, A Guzzo, G Fortino Internet of Things Meets Business Process Management: A Synergistic … , 2026 2026
GRACE-FL: Green Resource-Aware Communication-Efficient Federated Learning D Thakur, A Guzzo, G Fortino, SK Das IEEE Transactions on Artificial Intelligence , 2025 2025 Citations: 1
Quantization in Energy-Efficient Federated Learning F Ikram, D Thakur, A Guzzo, G Fortino 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 1-7 , 2025 2025
EAPD-CS: Energy Aware Performance Driven Client Selection in Federated Learning based Human Activity Recognition * D Thakur, A Guzzo, G Fortino 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC … , 2025 2025
Leveraging Cross-Silo Federated Learning in Process Mining [Short Paper] D Thakur, A Guzzo, G Fortino International Workshop on Leveraging Machine Learning in Process Mining … , 2025 2025
Anomalous Client Detection in Federated D Thakur, A Guzzo Intelligent Distributed Computing XVII: 17th International Symposium on … , 2025 2025
Analyzing the Fusion of Federated Learning and Large Language Model D Thakur, A Guzzo, G Fortino 2025 IEEE 5th International Conference on Human-Machine Systems (ICHMS), 282-288 , 2025 2025 Citations: 1
Green Federated Learning: A New Era of Green Aware AI D Thakur, A Guzzo, G Fortino, F Piccialli ACM Computing Surveys , 2025 2025 Citations: 67
Towards Secure and Scalable Energy Theft Detection: A Federated Learning Approach for Resource-Constrained Smart Meters D Labate, D Thakur, G Fortino IEEE Smart World Congress 2025, August 18 - 22, 2025, Calgary, Alberta, Canada , 2025 2025 Citations: 1
Preface to the Proceedings of Green-Aware AI 2024 R Cantini, DM Longo, D Thakur CEUR WORKSHOP PROCEEDINGS 3934 , 2025 2025
Multi-modal disease segmentation with continual learning and adaptive decision fusion X Xu, J Chen, D Thakur, D Hong Information Fusion 102962 , 2025 2025 Citations: 15
Client Specific Dynamic Aggregation for Non-IID Federated Learning V Altomare, D Thakur, A Guzzo, F Piccialli 2024 IEEE International Conference on Big Data (BigData), Washington, DC … , 2025 2025 Citations: 5
Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning D Thakur, A Guzzo, G Fortino, SK Das arXiv preprint arXiv:2412.11660 , 2024 2024 Citations: 2
Intelligent adaptive real-time monitoring and recognition system for human activities D Thakur, A Guzzo, G Fortino IEEE Transactions on Industrial Informatics 20 (11), 13212-13222 , 2024 2024 Citations: 23
Hardware-algorithm co-design of energy efficient federated learning in quantized neural network D Thakur, A Guzzo, G Fortino Internet of Things 26, 101223 , 2024 2024 Citations: 12
MOST CITED SCHOLAR PUBLICATIONS
Convae-lstm: Convolutional autoencoder long short-term memory network for smartphone-based human activity recognition D Thakur, S Biswas, ESL Ho, S Chattopadhyay IEEE Access 10, 4137-4156 , 2022 2022 Citations: 114
Smartphone based human activity monitoring and recognition using ML and DL: a comprehensive survey D Thakur, S Biswas Journal of Ambient Intelligence and Humanized Computing , 2020 2020 Citations: 71
Green Federated Learning: A New Era of Green Aware AI D Thakur, A Guzzo, G Fortino, F Piccialli ACM Computing Surveys , 2025 2025 Citations: 67
An Integration of feature extraction and Guided Regularized Random Forest feature selection for Smartphone based Human Activity Recognition D Thakur, S Biswas Journal of Network and Computer Applications 204 (103417) , 2022 2022 Citations: 59
Permutation importance based modified guided regularized random forest in human activity recognition with smartphone D Thakur, S Biswas Engineering Applications of Artificial Intelligence 129, 107681 , 2024 2024 Citations: 57
Feature fusion using deep learning for smartphone based human activity recognition D Thakur, S Biswas International Journal of Information Technology , 2021 2021 Citations: 56
Attention-based multihead deep learning framework for online activity monitoring with smartwatch sensors D Thakur, A Guzzo, G Fortino IEEE Internet of Things Journal 10 (20), 17746-17754 , 2023 2023 Citations: 44
Guided regularized random forest feature selection for smartphone based human activity recognition D Thakur, S Biswas Journal of Ambient Intelligence and Humanized Computing 14 (7), 9767-9779 , 2023 2023 Citations: 37
Attention-based deep learning framework for hemiplegic gait prediction with smartphone sensors D Thakur, S Biswas IEEE Sensors Journal 22 (12), 11979-11988 , 2022 2022 Citations: 26
Intelligent adaptive real-time monitoring and recognition system for human activities D Thakur, A Guzzo, G Fortino IEEE Transactions on Industrial Informatics 20 (11), 13212-13222 , 2024 2024 Citations: 23
A Novel Smartphone-Based Human Activity Recognition Approach using Convolutional Autoencoder Long Short-Term Memory Network D Thakur, S Roy, S Biswas, ESL Ho, S Chattopadhyay, S Shetty 24th International Conference on Information Reuse and Integration for Data … , 2023 2023 Citations: 22
Online change point detection in application with transition-aware activity recognition D Thakur, S Biswas IEEE Transactions on Human-Machine Systems 52 (6), 1176-1185 , 2022 2022 Citations: 17
Load balancing in software defined network P Kumari, D Thakur International Journal of Computer Sciences and Engineering 5 (12), 227-232 , 2017 2017 Citations: 17
Multi-modal disease segmentation with continual learning and adaptive decision fusion X Xu, J Chen, D Thakur, D Hong Information Fusion 102962 , 2025 2025 Citations: 15
Hardware-algorithm co-design of energy efficient federated learning in quantized neural network D Thakur, A Guzzo, G Fortino Internet of Things 26, 101223 , 2024 2024 Citations: 12
t-SNE and PCA in ensemble learning based human activity recognition with smartwatch D Thakur, A Guzzo, G Fortino 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), 1-6 , 2021 2021 Citations: 12
Multi-domain virtual network embedding with dynamic flow migration in software-defined networks D Thakur, M Khatua Journal of Network and Computer Applications 162, 102639 , 2020 2020 Citations: 12
Human activity recognition: trends and challenges D Thakur, A Pal Activity Recognition and Prediction for Smart IoT Environments, 161-182 , 2024 2024 Citations: 6
Subsampled randomized hadamard transformation-based ensemble extreme learning machine for human activity recognition D Thakur, A Pal ACM Transactions on Computing for Healthcare 5 (1), 1-23 , 2024 2024 Citations: 6
Energy Aware Federated Learning with Application of Activity Recognition D Thakur, A Guzzo, G Fortino 2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf … , 2023 2023 Citations: 6
