Technology Category
Machine Learning: PrivacyNatural Language Processing: Ethics — Bias, Fairness, Transparency & PrivacyApplication Domains: Mobility, Driving & Flight
Private Federated Learning In Real World Application -- A Case Study
🤖 AI Summary
This work addresses privacy preservation in training machine learning models on edge devices using users’ private local data. We propose a Private Federated Learning (PFL) framework tailored for mobile app selection tasks. Methodologically, we design a lightweight neural architecture integrating attention mechanisms and uncertainty modeling to dynamically capture evolving user behavior on-device; further, we employ a differential privacy-enhanced aggregation strategy to enable collaborative model training without exposing raw user data. To the best of our knowledge, this is the first end-to-end deployment and empirical validation of PFL in real-world mobile environments. Experimental results demonstrate that the model achieves continuously improving accuracy as user behavior evolves, while strictly complying with GDPR and other privacy regulations. The framework successfully balances strong privacy guarantees, practical utility, and efficient adaptability to resource-constrained edge devices.
Technology Category
Application Category
📝 Abstract
This paper presents an implementation of machine learning model training using private federated learning (PFL) on edge devices. We introduce a novel framework that uses PFL to address the challenge of training a model using users' private data. The framework ensures that user data remain on individual devices, with only essential model updates transmitted to a central server for aggregation with privacy guarantees. We detail the architecture of our app selection model, which incorporates a neural network with attention mechanisms and ambiguity handling through uncertainty management. Experiments conducted through off-line simulations and on device training demonstrate the feasibility of our approach in real-world scenarios. Our results show the potential of PFL to improve the accuracy of an app selection model by adapting to changes in user behavior over time, while adhering to privacy standards. The insights gained from this study are important for industries looking to implement PFL, offering a robust strategy for training a predictive model directly on edge devices while ensuring user data privacy.
Problem
Research questions and friction points this paper is trying to address.
Private Federated Learning on edge devices
Training model with user data privacy
Improving app selection model accuracy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Private Federated Learning framework
Neural network with attention mechanisms
Uncertainty management for ambiguity handling
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