Decentralized Machine Learning Training: A Survey on Synchronization, Consolidation, and Topologies

Khan, Qazi Waqas; Khan, Anam Nawaz; Rizwan, Atif; Ahmad, Rashid; Khan, Salabat; Kim, Do‐Hyeun

doi:10.1109/access.2023.3284976

Cited by 5 publications

(1 citation statement)

References 73 publications

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“…These shortcomings are effectively addressed through the implementation of federated learning in our research. By adopting a decentralized approach, federated learning [8] ensures that user data remains securely stored on individual devices, preserving the confidentiality of personal conversations and online activities. This privacy-preserving methodology encourages greater participation from users and institutions, leading to a more comprehensive and representative dataset.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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

Self Cite

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Adaptive DFL‐based straggler mitigation mechanism for synchronous ring topology in digital twin networks

Waqas Khan,

Park,

Ahmad

et al. 2024

IET Collab Intel Manufact

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Decentralised federated learning (DFL) transforms collaborative energy consumption prediction using distributed computation across a large network of edge nodes, ensuring data confidentiality by eliminating central data aggregation. Preserving individual privacy in energy forecasting is paramount, as it safeguards personal data from unauthorised examination. This highlights the importance of effectively handling local data to provide privacy protection. The authors proposed a DFL framework for residential energy forecasting, focusing on improving the performance and convergence of the collaborative model. The proposed framework enables local training of the long short‐term memory model with real‐time household energy data in a ring topology. Importantly, the framework addresses the issue of straggler nodes, nodes that lag in computation or communication, by proposing a heuristic straggler identification and mitigation mechanism to reduce their negative impact on overall system performance and communication efficiency. This approach improves collaborative energy prediction performance and ensures an overall reduction in waiting time, thus improving the convergence performance. Experimental results consistently demonstrate a low mean absolute error ranging from 3 to 3.2 across all edge nodes. The empirical findings unequivocally illustrate the efficiency of the proposed DFL architecture, highlighting its ability to improve communication efficiency and concurrently enhance performance.

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Decentralized and Distributed Learning for AIoT: A Comprehensive Review, Emerging Challenges, and Opportunities

Xu,

Seng,

Ang

et al. 2024

IEEE Access

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Decentralized Machine Learning Training: A Survey on Synchronization, Consolidation, and Topologies

Cited by 5 publications

References 73 publications

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

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

Adaptive DFL‐based straggler mitigation mechanism for synchronous ring topology in digital twin networks

Decentralized and Distributed Learning for AIoT: A Comprehensive Review, Emerging Challenges, and Opportunities

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