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Personalized Federated Graph Learning On Non-Iid Electronic Health Records, Tao Tang, Zhuoyang Han, Zhen Cai, Shuo Yu, Xiaokang Zhou, Taiwo Oseni, Sajal K. Das
Personalized Federated Graph Learning On Non-Iid Electronic Health Records, Tao Tang, Zhuoyang Han, Zhen Cai, Shuo Yu, Xiaokang Zhou, Taiwo Oseni, Sajal K. Das
Computer Science Faculty Research & Creative Works
Understanding The Latent Disease Patterns Embedded In Electronic Health Records (EHRs) Is Crucial For Making Precise And Proactive Healthcare Decisions. Federated Graph Learning-Based Methods Are Commonly Employed To Extract Complex Disease Patterns From The Distributed EHRs Without Sharing The Client-Side Raw Data. However, The Intrinsic Characteristics Of The Distributed EHRs Are Typically Non-Independent And Identically Distributed (Non-IID), Significantly Bringing Challenges Related To Data Imbalance And Leading To A Notable Decrease In The Effectiveness Of Making Healthcare Decisions Derived From The Global Model. To Address These Challenges, We Introduce A Novel Personalized Federated Learning Framework Named PEARL, Which Is Designed For …
Resource Aware Clustering For Tackling The Heterogeneity Of Participants In Federated Learning, Rahul Mishra, Hari Prabhat Gupta, Garvit Banga, Sajal K. Das
Resource Aware Clustering For Tackling The Heterogeneity Of Participants In Federated Learning, Rahul Mishra, Hari Prabhat Gupta, Garvit Banga, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated Learning Is A Training Framework That Enables Multiple Participants To Collaboratively Train A Shared Model While Preserving Data Privacy. The Heterogeneity Of Devices And Networking Resources Of The Participants Delay The Training And Aggregation. The Paper Introduces A Novel Approach To Federated Learning By Incorporating Resource-Aware Clustering. This Method Addresses The Challenges Posed By The Diverse Devices And Networking Resources Among Participants. Unlike Static Clustering Approaches, This Paper Proposes A Dynamic Method To Determine The Optimal Number Of Clusters Using Dunn Indices. It Enables Adaptability To The Varying Heterogeneity Levels Among Participants, Ensuring A Responsive And Customized Approach To …
Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das
Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das
Computer Science Faculty Research & Creative Works
With the enhancement of people's living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. As household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels …
One-Shot Federated Learning For Leo Constellations That Reduces Convergence Time From Days To 90 Minutes, Mohamed Elmahallawy, Tie (Tony) Tie Luo
One-Shot Federated Learning For Leo Constellations That Reduces Convergence Time From Days To 90 Minutes, Mohamed Elmahallawy, Tie (Tony) Tie Luo
Computer Science Faculty Research & Creative Works
A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft tracking. Machine learning can be utilized to analyze these mobility data to address global challenges, and Federated Learning (FL) is a promising approach because it eliminates the need for transmitting raw data and hence is both bandwidth and privacy friendly. However, FL requires many communication rounds between clients (satellites) and the parameter …
Robust Federated Learning Against Backdoor Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das
Robust Federated Learning Against Backdoor Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated Learning is a Privacy-Preserving Alter-Native for Distributed Learning with No Involvement of Data Transfer. as the Server Does Not Have Any Control on Clients' Actions, Some Adversaries May Participate in Learning to Introduce Corruption into the Underlying Model. Backdoor Attacker is One Such Adversary Who Injects a Trigger Pattern into the Data to Manipulate the Model Outcomes on a Specific Sub-Task. This Work Aims to Identify Backdoor Attackers and to Mitigate their Effects by Isolating their Weight Updates. Leveraging the Correlation between Clients' Gradients, We Propose Two Graph Theoretic Algorithms to Separate Out Attackers from the Benign Clients. under …
Fedvcp: A Federated-Learning-Based Cooperative Positioning Scheme For Social Internet Of Vehicles, Xiangjie Kong, Haoran Gao, Guojiang Shen, Gaohui Duan, Sajal K. Das
Fedvcp: A Federated-Learning-Based Cooperative Positioning Scheme For Social Internet Of Vehicles, Xiangjie Kong, Haoran Gao, Guojiang Shen, Gaohui Duan, Sajal K. Das
Computer Science Faculty Research & Creative Works
Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes …
Securing Federated Learning Against Overwhelming Collusive Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das
Securing Federated Learning Against Overwhelming Collusive Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das
Computer Science Faculty Research & Creative Works
In the era of a data-driven society with the ubiquity of Internet of Things (IoT) devices storing large amounts of data localized at different places, distributed learning has gained a lot of traction, however, assuming independent and identically distributed data (iid) across the devices. While relaxing this assumption that anyway does not hold in reality due to the heterogeneous nature of devices, federated learning (FL) has emerged as a privacy-preserving solution to train a collaborative model over non-iid data distributed across a massive number of devices. However, the appearance of malicious devices (attackers), who intend to corrupt the FL model, …
Leveraging Spanning Tree To Detect Colluding Attackers In Federated Learning, Priyesh Ranjan, Federico Coro, Ashish Gupta, Sajal K. Das
Leveraging Spanning Tree To Detect Colluding Attackers In Federated Learning, Priyesh Ranjan, Federico Coro, Ashish Gupta, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated learning distributes model training among multiple clients who, driven by privacy concerns, perform training using their local data and only share model weights for iterative aggregation on the server. In this work, we explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. Finally, we validate the effectiveness of our algorithm in presence of varying number of attackers on a classification task using a well-known Fashion-MNIST dataset.