Decentralized EM Algorithm for Gaussian Mixtures under Data Heterogeneity and Partial Labeling
arXiv:2411.05591v2 Announce Type: replace-cross Abstract: We systematically study several network-based Expectation-Maximization (EM) algorithms for the Gaussian mixture model within decentralized federated learning (DFL). Our theoretical investigation shows that directly extending the classic EM algorithm to DFL leads to a biased estimator when data are heterogeneously distributed across sites. To address this, we introduce a momentum network EM (MNEM) algorithm, which integrates information from both current and historical estimators from previous DFL iterations. We further develop a semi-su
The increasing focus on federated learning and decentralized AI necessitates robust algorithms that can handle data heterogeneity and partial labeling which are common in real-world distributed systems.
This research provides a foundational improvement to decentralized machine learning, making federated AI more practical and reliable for sensitive and distributed datasets, impacting various industries leveraging AI.
The introduction of the momentum network EM (MNEM) algorithm addresses biases in decentralized federated learning, improving accuracy and efficiency in distributed AI model training.
- · Federated learning platforms
- · Healthcare and finance sectors
- · Privacy-focused AI companies
- · Edge AI developers
- · Centralized data analytics firms (competitive pressure)
Improved performance and reliability of decentralized AI applications across various sectors.
Increased adoption of federated learning in scenarios where data privacy and sovereignty are paramount, potentially accelerating new business models.
Enhanced ability to train powerful AI models using globally distributed, sensitive data without ever centralizing it, leading to novel forms of cross-organizational collaboration.
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