Advancements in Federated Learning: PrivacyPreserving AI for Distributed Data Processing
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P101Keywords:
Federated Learning, Privacy-Preserving AI, Distributed Data, Machine Learning, Data Privacy, Encryption, 5G, BlockchainAbstract
Federated Learning (FL) has emerged as a revolutionary machine learning approach, enabling the training of algorithms across decentralized devices or servers while maintaining data privacy. Unlike traditional centralized methods that pool data into a single repository, FL keeps data localized, enhancing the protection of sensitive information and ensuring compliance with privacy standards like GDPR and CCPA. This paradigm shift is particularly relevant in today's data-driven world, where concerns over data breaches and regulatory compliance are paramount. FL allows organizations and individuals to collaboratively train powerful machine learning models without sharing sensitive data. By adopting FL approaches, leveraging distributed data and computing power across different sources while respecting user privacy becomes possible. The architecture of FL involves a central system coordinating updates from multiple sources to improve a global model. Edge devices, such as smartphones or IoT devices, perform local training using their unique datasets. Each edge device trains the model locally, sending only updates (like gradients) to the central server, ensuring sensitive data is never exposed1. Furthermore, privacy-preserving technologies like differential privacy and homomorphic encryption strengthen data confidentiality and compliance with regulations. Differential privacy introduces noise to data or model updates to prevent the reconstruction of individual information, while homomorphic encryption allows computations on encrypted data without decryption1. The rise of 5G networks will significantly enhance FL by reducing latency and improving communication between edge devices and central servers, enabling faster model training and real-time applications. Blockchain technology offers a decentralized and immutable ledger for tracking data usage and model updates, creating a transparent and tamper-proof mechanism, addressing trust issues in federated systems and further strengthening security
References
1. ACM Digital Library. (2024). Efficient dynamic federated learning for imbalanced data. https://www.vldb.org/pvldb/vol17/p2077-li.pdf
2. AIMultiple. Federated learning: Applications and use cases. https://research.aimultiple.com/federated-learning/
3. ArXiv. (2024). Advancements in federated learning for privacy-preserving AI. https://arxiv.org/abs/2404.15381
4. DataCamp. Federated learning: A guide to decentralized AI training. https://www.datacamp.com/blog/federated-learning
5. Digica. Federated learning: Challenges and future directions. https://digica.com/blog/federated-learning-part-2.html
6. IEEE Xplore. (2024). Privacy-preserving techniques in federated learning. https://ieeexplore.ieee.org/document/10150228/
7. IBM Research. What is federated learning? https://research.ibm.com/blog/what-is-federated-learning
8. MDPI. (2024). Federated learning for secure AI applications. https://www.mdpi.com/1999-5903/16/11/415
9. Nature. (2024). Recent advances in federated learning and AI privacy. https://www.nature.com/articles/s41598-024-81732-0
10. NeuralConcept. How is predictive AI impacting engineering? https://www.neuralconcept.com/post/how-is-predictive-aiimpacting-engineering
11. OpenMined. Advances and open problems in federated learning. https://openmined.org/blog/advances-and-openproblems-in-federated-learning/
12. PixelPlex. Federated learning guide: Benefits and challenges. https://pixelplex.io/blog/federated-learning-guide/
13. ResearchGate. (2024). Optimizing federated learning for decentralized AI models.
14. Scirp. (2024). Federated learning: Emerging trends and challenges. https://www.scirp.org/journal/paperinformation?paperid=136707
15. TensorOpera. Advances in federated learning for secure AI training. https://blog.tensoropera.ai/advances-in-federatedlearning/
16. V7 Labs. A comprehensive guide to federated learning. https://www.v7labs.com/blog/federated-learning-guide
17. Viso AI. Federated learning: A deep learning approach to AI security. https://viso.ai/deep-learning/federated-learning/
18. XenonStack. Federated learning applications in AI-driven systems. https://www.xenonstack.com/blog/federated-learningapplications
19. Wikipedia. Federated learning. https://en.wikipedia.org/wiki/Federated_learning
