- The paper presents a novel federated system that enables privacy-preserving on-device personalization in applications such as ASR and news content recommendation.
- It employs decentralized task scheduling with application-specific plug-ins and differential privacy mechanisms to tune models without exposing raw data.
- Demonstrated improvements include reduced word error rates in ASR and enhanced user engagement in news personalization, underscoring its practical impact.
Federated Evaluation and Tuning for On-Device Personalization: System Design and Applications
The paper presents a comprehensive analysis of the Federated Evaluation and Tuning (FET) system designed to facilitate on-device personalization of ML systems, emphasizing its relevance in maintaining user privacy. This system initially focused on optimizing parameters for automatic speech recognition (ASR) systems and has since been extended to include support for Federated Learning (FL).
The FET system is built to address modern challenges related to on-device processing and personalized user experiences, particularly in ML environments. It operates by evaluating and tuning device-specific parameters without global data exposure, preserving data privacy. The paper explores two significant use cases: news personalization and ASR optimization.
System Design
The core design of the FET system involves a task agnostic approach that supports varying types of federated tasks through application-specific plug-ins. This flexibility allows the system to perform arbitrary distributed computations across devices while maintaining a robust data privacy model. One of its chief considerations is on-device data handling—data is stored locally with strict retention policies and results are shared with the server only as aggregated metrics or statistically noised updates.
The on-device components, including a task scheduler and results manager, facilitate task execution based on pre-defined criteria. The results manager securely transports aggregated metrics or model updates back to the server. This decentralized yet secure mechanism is supported by task sampling strategies designed to regulate device participation, ensuring efficient system operation and minimal server load.
Federated Learning Extensions
FL support within the FET system was motivated by the need for efficient, privacy-preserving learning across widely distributed data sources. This extension includes the adoption of Federated Averaging, allowing the system to aggregate model updates without retaining raw data. Critical to these extensions is the addition of local and central Differential Privacy (DP) mechanisms, minimizing privacy risk while optimizing model quality across decentralized networks.
Applications
News Personalization
The FET system optimizes parameters for news personalization algorithms by analyzing user interactions with news content. The system performs federated tuning by executing randomized grid search trials to determine optimal parameter settings, with the aim of enhancing user engagement with news articles. The algorithm’s effectiveness is validated through consistent improvements in daily article views and user time spent.
Automatic Speech Recognition
Addressing the ASR system’s personalization through the FET platform involves leveraging on-device sources—contact names, search history, and more—to optimize speech recognition models. The system evaluates performance through word error rate estimation (eWER), enabling a semi-supervised learning approach that bypasses the need for exhaustive manual transcriptions. The results show significant improvements in recognizing user-specific vocabulary, with measurable reductions in word error rates through system combination.
Implications and Future Directions
The implications of the FET system are manifold, particularly in enhancing user-centric personalization while managing stringent data privacy requirements. By achieving a balance between individual model refinement and federated optimization, the framework addresses critical challenges in distributed ML infrastructure. Future work may expand on integrating more sophisticated FL algorithms within the FET framework and broadening its applicability across diverse application domains in AI systems. These paths open possibilities for improving model adaptability and user-tailored experiences in privacy-conscious settings.