Summary of Privacy-preserving Federated Learning with Differentially Private Hyperdimensional Computing, by Fardin Jalil Piran et al.
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
by Fardin Jalil Piran, Zhiling Chen, Mohsen Imani, Farhad Imani
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Federated HyperDimensional computing with Privacy-preserving (FedHDPrivacy) framework combines the neuro-symbolic paradigm with Differential Privacy (DP) mechanisms to ensure robust performance in Federated Learning (FL) applications. FedHDPrivacy addresses privacy concerns by carefully managing cumulative noise from previous rounds, adding only incremental noise to meet privacy requirements. In a real-world case study, FedHDPrivacy outperforms standard FL frameworks like Federated Averaging (FedAvg), Federated Stochastic Gradient Descent (FedSGD), and others by up to 38%. The framework’s potential for future enhancements, such as multimodal data fusion, is also highlighted. This approach contributes to the development of efficient and privacy-preserving FL methods in Internet of Things (IoT) environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning helps devices in the Internet of Things learn together without sharing all their data. However, this makes it vulnerable to attacks that can reveal sensitive information. To keep the data private, researchers added noise to the learning process. But this made the models less accurate over time. A new framework called FedHDPrivacy combines two ideas: using hidden patterns in neural networks and adding noise to protect privacy. This approach carefully adds just the right amount of noise to meet privacy requirements while still keeping the model accurate. In a real-world example, this approach worked better than other methods by up to 38%. It also has potential for future improvements. |
Keywords
» Artificial intelligence » Federated learning » Stochastic gradient descent
