専門家のミックスがパーソナライズされた:視覚言語モデルのための統合された即時学習
Jun Luo1, Chen Chen2, Shandong Wu3
1Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15213, USA.
... International Conference on Learning Representations
|February 16, 2026
まとめ
パーソナライズド・フェデレーテッド・ミックス・オブ・アダプティブ・プロンプト (pFedMoAP) は,クライアントが複数の非ローカルプロンプトを使用できるようにすることで,フェデレーテッド・学習を強化します. このアプローチは,さまざまなデータセットにおけるビジョン言語モデルのパーソナライゼーションとパフォーマンスを改善します.
科学分野:
- コンピュータサイエンス コンピュータサイエンス
- 人工知能 (AI) とは,人工知能 (AI) のことです.
- 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
背景:
- 連邦学習 (FL) は,通常,クライアントを単一のグローバルモデルに制限し,パーソナライゼーションを制限します.
- CLIPのようなVision-Language Models (VLMs) は,迅速な学習を通じて強力な表現学習を提供します.
- 既存の連結型即時学習方法は,従来のFLパラダイムの限界を継承しています.
研究 の 目的:
- 新しい連邦式プロンプト学習フレームワーク,パーソナライズド・フェデラート・ミックス・オブ・アダプティブ・プロンプト (pFedMoAP) を提案する.
- VLM用の軽量プロンプトの取り扱いに従来FLの限界に対処するために.
- クライアントが複数の事前に集約されたプロンプトを活用できるようにすることで,統合学習のパーソナライゼーションを強化します.
主な方法:
- 迅速な学習のための専門家ミックス (MoE) のアプローチを利用した連邦学習フレームワークであるpFedMoAPを導入しました.
- 顧客が複数の事前に集約されたプロンプトを固定された非ローカルな専門家としてダウンロードできるようにしました.
- パーソナライズされた即時適応と強化されたテキスト機能生成のためのローカル注意力ベースのゲーティングネットワークを実装しました.
主要な成果:
- 様々な連邦設定下での9つの多様なデータセットでpFedMoAPの有効性を実証しました.
- 適応プロンプトの専門家によるローカル画像データと強化されたテキスト機能の間の調整を改善しました.
- 連邦化された即時学習におけるパーソナライゼーションの利点を検証した.
結論:
- pFedMoAPは,専門家ミックス・フレームワーク内で非現地専門家を統合することによって,連邦的な即時学習を効果的にパーソナライズします.
- 提案された方法は,VLMの表現学習を連邦化された環境で強化し,従来のアプローチを上回ります.
- このフレームワークは,軽量なプロンプトでパーソナライズされた統合学習のための柔軟で効率的なソリューションを提供します.
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