FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

Shetty, Nisha P.; Muniyal, Balachandra; Priyanshu, Aman; Das, Vedant Rishi

doi:10.13052/jcsm2245-1439.1242

Cited by 3 publications

(2 citation statements)

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“…Muniyal el al. ( 3 ) introduced Federated Learning [FL] as a procedure to secure sensitive user data across the process pipeline. The authors emphasize more toward the possibility of a security breach on a Cyberbully detection and prevention system when the same is based on a Central Server.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This paper proposes a novel approach to combat cyberbullying by integrating findings from cyberbullying statistics with innovative solutions. Our approach involves the fusion of two cutting-edge technologies: Blockchain and Federated Learning (FL) ( 3 ). Blockchain, known for its decentralized nature and transaction integrity, serves as the foundation of our solution, while Federated Learning facilitates collaborative machine learning without compromising individual data privacy.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach toward cyberbullying with intelligent recommendations using deep learning based blockchain solution

Alabdali,

Mashat

2024

Front. Med.

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Integrating healthcare into traffic accident prevention through predictive modeling holds immense potential. Decentralized Defense presents a transformative vision for combating cyberbullying, prioritizing user privacy, fostering a safer online environment, and offering valuable insights for both healthcare and predictive modeling applications. As cyberbullying proliferates in social media, a pressing need exists for a robust and innovative solution that ensures user safety in the cyberspace. This paper aims toward introducing the approach of merging Blockchain and Federated Learning (FL), to create a decentralized AI solutions for cyberbullying. It has also used Alloy Language for formal modeling of social connections using specific declarations that are defined by the novel algorithm in the paper on two different datasets on Cyberbullying and are available online. The proposed novel method uses DBN to run established relation tests amongst the features in two phases, the first is LSTM to run tests to develop established features for the DBN layer and second is that these are run on various blocks of information of the blockchain. The performance of our proposed research is compared with the previous research and are evaluated using several metrics on creating the standard benchmarks for real world applications.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel approach toward cyberbullying with intelligent recommendations using deep learning based blockchain solution

Alabdali,

Mashat

2024

Front. Med.

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Safeguarding Online Spaces: A Powerful Fusion of Federated Learning, Word Embeddings, and Emotional Features for Cyberbullying Detection

Samee,

Khan,

Khan

et al. 2023

IEEE Access

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Cyberbullying has emerged as a pervasive issue in the digital age, necessitating advanced techniques for effective detection and mitigation. This research explores the integration of word embeddings, emotional features, and federated learning to address the challenges of centralized data processing and user privacy concerns prevalent in previous methods. Word embeddings capture semantic relationships and contextual information, enabling a more nuanced understanding of text data, while emotional features derived from text extend the analysis to encompass the affective dimension, enhancing cyberbullying identification. Federated learning, a decentralized learning paradigm, offers a compelling solution to centralizing sensitive user data by enabling collaborative model training across distributed devices, preserving privacy while harnessing collective intelligence. In this study, we conduct an in-depth investigation into the fusion of word embeddings, emotional features, and federated learning, complemented by the utilization of BERT, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) models. These techniques are applied in the context of cyberbullying detection, using publicly available multi-platform (social media) cyberbullying datasets. Through extensive experiments and evaluations, our proposed framework demonstrates superior performance and robustness compared to traditional methods. The results illustrate the enhanced ability to identify and combat cyberbullying incidents effectively, contributing to the creation of safer online environments. Particularly, the BERT model consistently outperforms other deep learning models (CNN, DNN, LSTM) in cyberbullying detection while preserving the privacy of local datasets for each social platform through our improved federated learning setup. We have provided Differential Privacy based security analysis for the proposed method to further strengthen the privacy and robustness of the system. By leveraging word embeddings, emotional features, and federated learning, this research opens new avenues in cyberbullying research, paving the way for proactive intervention and support mechanisms. The comprehensive approach presented herein highlights the substantial strengths and advantages of this integrated methodology, setting a foundation for future advancements in cyberbullying detection and mitigation.

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VirtuGuard: Ethically Aligned Artificial Intelligence Framework for Cyberbullying Mitigation

Wang,

Burken,

Sun

et al. 2024

2024 IEEE Conference on Artificial Intelligence (CAI)

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FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

Cited by 3 publications

References 0 publications

A novel approach toward cyberbullying with intelligent recommendations using deep learning based blockchain solution

A novel approach toward cyberbullying with intelligent recommendations using deep learning based blockchain solution

Safeguarding Online Spaces: A Powerful Fusion of Federated Learning, Word Embeddings, and Emotional Features for Cyberbullying Detection

VirtuGuard: Ethically Aligned Artificial Intelligence Framework for Cyberbullying Mitigation

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