FedEMA: 自動運転における負のエントロピー正則化を伴う連合指数移動平均
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
自動運転の連合学習で時間的な破滅的忘却を防ぐため、サーバ側で過去ラウンドのEMAモデルを融合し、車両側で負のエントロピー正則化を加えるFedEMAを提案し、mIoUを7.12%改善した。
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著者: Wei-Bin Kou, Guangxu Zhu, Bingyang Cheng, Shuai Wang, Ming Tang, Yik-Chung Wu
分類: cs.RO, cs.LG
原文アブストラクト
Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization due to domain-shift. Federated Learning (FL) has emerged as a promising paradigm for enhancing the generalization of AD models through privacy-preserving distributed learning. However, these FL AD models face significant temporal catastrophic forgetting when deployed in dynamically evolving environments, where continuous adaptation causes abrupt erosion of historical knowledge. This paper proposes Federated Exponential Moving Average (FedEMA), a novel framework that addresses this challenge through two integral innovations: (I) Server-side model's historical fitting capability preservation via fusing current FL round's aggregation model and a proposed previous FL round's exponential moving average (EMA) model; (II) Vehicle-side negative entropy regularization to prevent FL models' possible overfitting to EMA-introduced temporal patterns. Above two strategies empower FedEMA a dual-objective optimization that balances model generalization and adaptability. In addition, we conduct theoretical convergence analysis for the proposed FedEMA. Extensive experiments both on Cityscapes dataset and Camvid dataset demonstrate FedEMA's superiority over existing approaches, showing 7.12% higher mean Intersection-over-Union (mIoU).
関連論文
- Sense4FL: 自動運転の物体検出のための車両クラウドセンシング強化連合学習連合学習/自動運転
- 無信号交差点における連結自動運転車のための密度考慮型連合模倣学習連合学習/自動運転
