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FEDPIT: Privacy-preserving and Few-shot Federated Instruction Tuning

Core Concepts
FEDPIT proposes a novel federated algorithm that utilizes LLMs' in-context learning capability to generate task-specific synthetic data for training autonomously, improving federated few-shot performance while preserving privacy.
FEDPIT addresses challenges in federated instruction tuning by generating synthetic data and employing parameter-isolated training. It enhances performance and privacy protection against data extraction attacks. The method is tested on real-world medical data, demonstrating its effectiveness.
FEDIT assumes sufficient instruction data for model training, which is impractical in real-world applications. Nasr et al. (2023) suggest that the training data extraction attack is more prone to success in larger or overfitted models. Fig. 1 shows the progressively increasing privacy breach risk during the FEDIT training process.
"FEDPIT incorporates two crucial components into FEDIT: self-generation and parameter-isolated training." "Our method employs synthetic data to enhance federated few-shot performance while thwarting training data extraction attacks."

Key Insights Distilled From

by Zhuo Zhang,J... at 03-12-2024

Deeper Inquiries




Synthetic data generated by LLMs may have some drawbacks and limitations. One potential limitation is the risk of bias or lack of diversity in the synthetic data. Since LLMs learn from existing data, there is a possibility that the generated synthetic data may reflect biases present in the training data. Additionally, the quality of the synthetic data heavily relies on the prompt provided to the model, which can impact the relevance and accuracy of the generated examples. Moreover, relying solely on synthetic data may not capture real-world nuances or complexities that are present in actual user interactions or scenarios.

How can the concept of parameter-isolated training be applied to other types of machine learning algorithms

パラメーター隔離トレーニングのコンセプトは他種類の機械学習アルゴリズムにも適用することができます。 例えば、「フェデレーテッド・ラーニング」(FL)アプローチ内で異なるモデル間で共有されるパラメーター情報を保護しながらトレーニング効率性向上させる場合、「パラメーター隔離トレーニング」手法は非常に有益です。 この手法はFLフレームワーク全体または個々の参加者間で共有されたグローバルパラメーター(Wg)およびローカルパラメーター(Wl)間で厳密な区別を行い,個人的または標準化された局所データだけでは不十分だったり,攻撃リスク増大時でも安全性確保します。 これまでは主流技術中心だった「深層学習」という新しい方向性提案しました。「深層学研究」(DLR) その目的:「知識表現」「抽象化能力」「汎化能力」という三つキャッチフレースポット点灯します。