Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, thereby reducing privacy risks. However, enabling human trust and control over FL systems requires understanding the evolving behaviour of clients, whether beneficial or detrimental for the training, which still represents a key challenge in the current literature. To address this challenge, we introduce Federated Behavioural Planes (FBPs), a novel method to analyse, visualise, and explain the dynamics of FL systems, showing how clients behave under two different lenses: predictive performance (error behavioural space) and decision-making processes (counterfactual behavioural space). Our experiments demonstrate that FBPs provide informative trajectories describing the evolving states of clients and their contributions to the global model, thereby enabling the identification of clusters of clients with similar behaviours. Leveraging the patterns identified by FBPs, we propose a robust aggregation technique named Federated Behavioural Shields to detect malicious or noisy client models, thereby enhancing security and surpassing the efficacy of existing state-of-the-art FL defense mechanisms.

翻译：联邦学习（Federated Learning, FL）作为一种分布式深度学习环境中的隐私感知方法，允许多个客户端在不共享敏感数据的情况下协作训练模型，从而降低隐私风险。然而，要建立人类对FL系统的信任并实现对其的控制，需要理解客户端行为的演变——无论其对训练有益还是有害——这仍然是当前文献中的一个关键挑战。为应对这一挑战，我们提出了联邦行为平面（Federated Behavioural Planes, FBPs），这是一种分析、可视化和解释FL系统动态的新方法，它通过两种不同的视角展示客户端的行为：预测性能（误差行为空间）和决策过程（反事实行为空间）。我们的实验表明，FBPs能够提供描述客户端状态演变及其对全局模型贡献的信息化轨迹，从而识别出具有相似行为的客户端集群。利用FBPs识别的模式，我们提出了一种名为联邦行为护盾（Federated Behavioural Shields）的鲁棒聚合技术，用于检测恶意或含噪声的客户端模型，从而增强安全性并超越现有先进FL防御机制的有效性。