エッジAIとフェデレーションシステムにおける機能圧縮のための適応情報限定マッピング
1Vinnytsia National Technical University, Vinnytsia, Ukraine. kovtun_v_v@vntu.edu.ua.
Scientific reports
|August 22, 2025
まとめ
この研究は,エッジAIとフェデラテッドラーニングにおける効率的な機能圧縮のためのエントロピー調節モデルを導入します. データの負荷を大幅に減らし,リソースが限られているデバイスで高い精度と意味学的な信頼性を保ちます.
科学分野:
- 人工知能
- 機械学習
- データ圧縮
背景:
- 分散型インテリジェントシステムは 限られたリソースで課題に直面し,エッジAIとフェデレーション・ラーニングに影響を与えます.
- 通信オーバーヘッドの削減は,不安定なサービス品質,限られた帯域幅,およびデータの異質性のために不可欠です.
研究 の 目的:
- 資源制限の分散型インテリジェントシステムの効率的な機能圧縮モデルを開発する.
- エッジAIとフェデレーテッドラーニング環境における通信オーバーヘッドの削減の必要性を解決する.
主な方法:
- 変数的な潜伏マッピング,非負性制約投影,ストキャスティックブール変換を組み合わせた新しいエントロピー調節圧縮モデルを開発した.
- クールバック-ライブラー分岐と意味論的関連性保存を含む一般化された圧縮品質機能を提案した.
- 制限された計算環境のための効率的なプロジェクション・グラデーション最適化アルゴリズムを設計した.
主要な成果:
- HARとPAMAP2のデータセットのエントロピー負荷を6倍に削減しました.
- 94%以上の分類精度を維持し,高セマンティックフィデリティ.
- 低電力機器 (ジェットソン・ナノ,ラズベリー・パイ4) のノイズと損失に対する堅実性を証明した.
結論:
- 提案されたモデルは,SOTAソリューションと比較して優れた圧縮効率,適応性,および安定性を提供します.
- このアプローチは,特に不安定な伝送条件下で,エッジAIとフェデレーション・ラーニングに有効です.
- リアルなデータセットと低電力ハードウェアでの実用的な有効性を検証した.
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