Mr. Pretom Roy Ovi Profile

Pretom Roy Ovi

at Univ of Maryland Baltimore County

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Proceedings Article | 12 June 2023 Presentation + Paper

Confident federated learning to tackle label flipped data poisoning attacks

Pretom Roy Ovi, Aryya Gangopadhyay, Robert Erbacher, Carl Busart

Proceedings Volume 12538, 125380Z (2023) https://doi.org/10.1117/12.2663911

KEYWORDS: Machine learning, Data privacy, Adversarial training

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Federated Learning (FL) enables collaborative model building among a large number of participants without revealing the sensitive data to the central server. However, because of its distributed nature, FL has limited control over the local data and corresponding training process. Therefore, it is susceptible to data poisoning attacks where malicious workers use malicious training data to train the model. Furthermore, attackers on the worker side can easily manipulate local data by swapping the labels of training instances to initiate data poisoning attacks. And local workers under such attacks carry incorrect information to the server, poison the global model, and cause misclassifications. So, detecting and preventing poisonous training samples from local training is crucial in federated training. To address it, we propose a federated learning framework, namely Confident Federated Learning to prevent data poisoning attacks on local workers. Here, we first validate the label quality of training samples by characterizing and identifying label errors in the training data and then exclude the detected mislabeled samples from the local training. To this aim, we experiment with our proposed approach on MNIST, Fashion-MNIST, and CIFAR-10 dataset and experimental results validated the robustness of the proposed framework against the data poisoning attacks by successfully detecting the mislabeled samples with above 85% accuracy.

Proceedings Article | 6 June 2022 Presentation + Paper

Towards developing a data security aware federated training framework in multi-modal contested environments

Pretom Roy Ovi, Emon Dey, Nirmalya Roy, Aryya Gangopadhyay, Robert Erbacher

Proceedings Volume 12113, 121130M (2022) https://doi.org/10.1117/12.2618904

KEYWORDS: Data modeling, Computer security, Visualization, Neurons, Instrument modeling, Data communications, Acoustics, Profiling, Data acquisition, Visual process modeling

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Secure data communication is crucial in contested environments such as battlefields. In such environments, there is always risk of data breach through unauthorized interceptions. This may lead to unauthorized access to tactical information and infiltration into the systems. In this work, we propose a detailed training setup in the federated learning framework for object classification where the raw data will be maintained locally at the edge devices and will not be shared with a central server or with each other. The server sends a global model to edge devices, which is then trained locally at the edge, and the updated parameters are sent back to the central server, where they are aggregated, which takes place iteratively. This setup ensures robustness against malicious cyberattacks as well as reduce communication overhead. Furthermore, to tackle the irregularity in object classification task with a single data modality in such contested environment, a deep learning model incorporating multiple modalities is used as the global model in our proposed federated learning setup. This model can serve as a possible solution in object identification with multi-modal data. We conduct a comprehensive analysis on the importance of multi-modal approach compared to individual modalities within our proposed federate learning setup. We also provide a resource profiling based on memory requirements, training time, and energy usage on two resource constrained devices to demonstrate the feasibility of the proposed approach.

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