发表机构
TU Delft（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出FedCKA，利用中心核对齐动态选择表示一致的层进行个性化，在跨域联邦3D感知中优于现有基线，平均NDS提升7个百分点。
AI 中文摘要
智能车辆的鲁棒感知要求3D目标检测器在域偏移（如一天中的时间、地点或天气变化）下保持可靠。然而，由于标注成本高昂且偏移罕见，某些环境缺乏足够的数据来训练独立的检测器。联邦学习提供了一种隐私保护的协作模型训练框架，使客户端能够受益于跨多样环境的共享学习。然而，该框架传统上依赖单一的全局共识模型，难以在异构的本地数据分布上表现良好。通过调整模型的一部分可以更好地捕捉本地条件，但许多个性化方法依赖于预定义的层划分或固定的个性化比例，从而限制了适应客户端特定差异的能力。为减少这种刚性，我们提出FedCKA，一种基于中心核对齐（CKA）的策略，动态处理个性化与全局化的权衡。具体而言，FedCKA在训练期间计算本地客户端模型与全局共识模型之间的逐层特征相似性。通过将逐层相似性分数转换为客户端特定的聚合掩码，FedCKA选择性地共享表示一致的层。在基于nuScenes的统一多域基准上的评估表明，FedCKA优于已建立的联邦基线，包括FedBN、FedRep和FedSelect，将平均NDS比最强基线提高了7个百分点。研究结果既提供了比较基准，也为跨地点、天气和光照变化的鲁棒联邦3D感知提供了有前景的方向。代码可在以下https URL获取。
英文摘要
Robust perception in intelligent vehicles demands 3D object detectors that remain dependable under domain shifts, such as changes in time of day, location, or weather. However, due to costly annotation and rare shifts, some environments lack sufficient data to train a standalone detector. Federated learning offers a privacy-preserving framework for collaborative model training, enabling clients to benefit from shared learning across diverse environments. Yet, this framework traditionally relies on a single global consensus model, which struggles to perform across heterogeneous local data distributions. Local conditions are better captured by adapting a subset of the model, but many personalization approaches rely on predefined layer partitions or fixed personalization ratios, thereby limiting adaptation to client-specific divergence. To reduce this rigidity, we propose FedCKA, a Centered Kernel Alignment (CKA)-based strategy that dynamically handles the personalization-globalization trade-off. Specifically, FedCKA computes layer-wise feature similarities between local client models and the global consensus model during training. By converting layer-wise similarity scores into client-specific aggregation masks, FedCKA selectively shares representation-consistent layers. Evaluation on a unified multi-domain benchmark based on nuScenes shows that FedCKA outperforms established federated baselines, including FedBN, FedRep, and FedSelect, improving average NDS by 7 percentage points over the strongest baseline. The findings offer both a comparative benchmark and a promising direction for robust federated 3D perception across shifts in location, weather, and illumination. Code is available at https://github.com/j-verhoog/FedCKA.
Comments8 pages, 3 figures. Submitted to IEEE ICRA 2027
