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Related Experiment Video

Updated: Nov 23, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Probabilistic Predictions with Federated Learning.

Adam Thor Thorgeirsson^1,2, Frank Gauterin²

¹Dr. Ing. h.c. F. Porsche AG, 71287 Weissach, Germany.

Entropy (Basel, Switzerland)

|January 5, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for probabilistic machine learning in federated learning settings. It enables accurate predictions with uncertainty quantification, matching non-distributed model performance.

Keywords:

Bayesian deep learning federated learning predictive uncertainty probabilistic machine learning

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Last Updated: Nov 23, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Published on: January 26, 2024

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Machine Learning
Distributed Systems
Bayesian Inference

Background:

Probabilistic predictions are crucial in machine learning but often computationally expensive with Bayesian methods.
Federated learning offers efficient and private training on distributed data but lacks predictive uncertainty.
Existing methods struggle to balance computational cost, privacy, and uncertainty quantification in federated settings.

Purpose of the Study:

To develop a novel approach for incorporating predictive uncertainty into federated learning.
To address the computational expense and privacy concerns of traditional Bayesian methods in distributed environments.
To enable accurate probabilistic predictions within a federated learning framework.

Main Methods:

Treating local model weights as a posterior distribution during the aggregation step.
Modifying the aggregation process in federated learning to include uncertainty.
Comparing the proposed method against state-of-the-art Bayesian and non-Bayesian probabilistic learning algorithms.

Main Results:

The proposed federated learning approach successfully incorporates predictive uncertainty.
Performance was evaluated using proper scoring rules on predictive distributions.
The method achieved performance comparable to non-distributed benchmarks.

Conclusions:

This novel aggregation strategy effectively introduces uncertainty into federated learning.
The approach offers a viable solution for probabilistic predictions in distributed, privacy-preserving machine learning.
It bridges the gap between efficient federated learning and the need for uncertainty quantification.