Summary of Federated Neural Nonparametric Point Processes, by Hui Chen et al.
Federated Neural Nonparametric Point Processes
by Hui Chen, Xuhui Fan, Hengyu Liu, Yaqiong Li, Zhilin Zhao, Feng Zhou, Christopher John Quinn, Longbing Cao
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 This paper proposes a novel approach to modeling temporal point processes (TPPs) in federated systems, which are designed to handle sparse and uncertain event occurrences while prioritizing privacy. The Federated neural nonparametric Point Process model, or FedPP, integrates neural embeddings into Sigmoidal Gaussian Cox Processes (SGCPs) on the client side, allowing it to capture client-specific event dynamics and uncertainties. For global aggregation, FedPP introduces a divergence-based mechanism that communicates kernel hyperparameters between the server and clients while keeping client-specific parameters local. The paper demonstrates FedPP’s superior performance in federated settings using KL divergence and Wasserstein distance-based global aggregation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated systems need to handle sparse and uncertain event occurrences, like predicting when someone will get a flu shot or book an appointment. A type of math called temporal point processes (TPPs) is good for this, but it can be tricky when many people’s data are combined while keeping personal information private. This paper proposes a new way to do TPPs in federated systems that balances privacy and accuracy. It uses neural networks to understand each person’s unique patterns and combines them in a way that keeps sensitive information local. |
