Machine Unlearning is an emerging field that addresses data privacy issues by
enabling the removal of private or irrelevant data from the Machine Learning
process. Challenges related to privacy and model efficiency arise from the use
of outdated, private, and irrelevant data. These issues compromise both the
accuracy and the computational efficiency of models in both Machine Learning
and Unlearning. To mitigate these challenges, we introduce a novel framework,
Attention-based Machine Unlearning using Federated Reinforcement Learning
(FRAMU). This framework incorporates adaptive learning mechanisms, privacy
preservation techniques, and optimization strategies, making it a well-rounded
solution for handling various data sources, either single-modality or
multi-modality, while maintaining accuracy and privacy. FRAMU's strength lies
in its adaptability to fluctuating data landscapes, its ability to unlearn
outdated, private, or irrelevant data, and its support for continual model
evolution without compromising privacy. Our experiments, conducted on both
single-modality and multi-modality datasets, revealed that FRAMU significantly
outperformed baseline models. Additional assessments of convergence behavior
and optimization strategies further validate the framework's utility in
federated learning applications. Overall, FRAMU advances Machine Unlearning by
offering a robust, privacy-preserving solution that optimizes model performance
while also addressing key challenges in dynamic data environments.

Machine Unlearning 的一个新颖框架是 Attention-based Machine Unlearning using Federated Reinforcement Learning (FRAMU)，通过自适应学习机制、隐私保护技术和优化策略，解决了数据隐私和模型效率方面的挑战，并在动态数据环境中提供了一个强大的隐私保护解决方案，优化了模型性能。

FRAMU: 基于注意力的联邦加强学习的机器消去

FRAMU: Attention-based Machine Unlearning using Federated Reinforcement  Learning

Personal Digital Assistants (PDAs) - such as Siri, Alexa and Google
Assistant, to name a few - play an increasingly important role to access
information and complete tasks spanning multiple domains, and by diverse groups
of users. A text-to-speech (TTS) module allows PDAs to interact in a natural,
human-like manner, and play a vital role when the interaction involves people
with visual impairments or other disabilities. To cater to the needs of a
diverse set of users, inclusive TTS is important to recognize and pronounce
correctly text in different languages and dialects. Despite great progress in
speech synthesis, the pronunciation accuracy of named entities in a
multi-lingual setting still has a large room for improvement. Existing
approaches to correct named entity (NE) mispronunciations, like retraining
Grapheme-to-Phoneme (G2P) models, or maintaining a TTS pronunciation
dictionary, require expensive annotation of the ground truth pronunciation,
which is also time consuming. In this work, we present a highly-precise,
PDA-compatible pronunciation learning framework for the task of TTS
mispronunciation detection and correction. In addition, we also propose a novel
mispronunciation detection model called DTW-SiameseNet, which employs metric
learning with a Siamese architecture for Dynamic Time Warping (DTW) with
triplet loss. We demonstrate that a locale-agnostic, privacy-preserving
solution to the problem of TTS mispronunciation detection is feasible. We
evaluate our approach on a real-world dataset, and a corpus of NE
pronunciations of an anonymized audio dataset of person names recorded by
participants from 10 different locales. Human evaluation shows our proposed
approach improves pronunciation accuracy on average by ~6% compared to strong
phoneme-based and audio-based baselines.

本研究提出了一种针对 TTS 语音合成任务中的发音错误检测和校正的高精度、适用于 PDA 的发音学习框架，同时还提出了一种基于动态时间规整技术和度量学习的发音错误检测模型 DTW-SiameseNet。结果表明，与基于音素和音频的基线相比，我们的方法平均提高了～6％ 的发音准确率，并且此方法不受语言和隐私问题的影响。