Machine learning (ML) based indoor localization solutions are critical for many emerging applications, yet their efficacy is often compromised by hardware/software variations across mobile devices (i.e., device heterogeneity) and the threat of ML data poisoning attacks. Conventional methods aimed at countering these challenges show limited resilience to the uncertainties created by these phenomena. In response, in this paper, we introduce SAFELOC, a novel framework that not only minimizes localization errors under these challenging conditions but also ensures model compactness for efficient mobile device deployment. Our framework targets a distributed and co-operative learning environment that uses federated learning (FL) to preserve user data privacy and assumes heterogeneous mobile devices carried by users (just like in most real-world scenarios). Within this heterogeneous FL context, SAFELOC introduces a novel fused neural network architecture that performs data poisoning detection and localization, with a low model footprint. Additionally, a dynamic saliency map-based aggregation strategy is designed to adapt based on the severity of the detected data poisoning scenario. Experimental evaluations demonstrate that SAFELOC achieves improvements of up to 5.9x in mean localization error, 7.8x in worst-case localization error, and a 2.1x reduction in model inference latency compared to state-of-the-art indoor localization frameworks, across diverse building floorplans, mobile devices, and ML data poisoning attack scenarios.

翻译：基于机器学习（ML）的室内定位解决方案对许多新兴应用至关重要，但其效能常因移动设备间的硬件/软件差异（即设备异构性）以及ML数据投毒攻击的威胁而受损。旨在应对这些挑战的传统方法对这些现象所产生的不确定性表现出有限的韧性。为此，本文中我们提出了SAFELOC，这是一个新颖的框架，它不仅能在这些挑战性条件下最小化定位误差，还能确保模型紧凑性以便于在移动设备上高效部署。我们的框架针对一个分布式协同学习环境，该环境利用联邦学习（FL）来保护用户数据隐私，并假设用户携带异构的移动设备（正如大多数现实场景一样）。在此异构FL背景下，SAFELOC引入了一种新颖的融合神经网络架构，该架构以较低的模型占用空间执行数据投毒检测与定位。此外，设计了一种基于动态显著图的聚合策略，该策略能够根据检测到的数据投毒场景的严重程度进行自适应调整。实验评估表明，与最先进的室内定位框架相比，SAFELOC在不同的建筑平面图、移动设备和ML数据投毒攻击场景下，实现了平均定位误差高达5.9倍的提升，最坏情况定位误差高达7.8倍的提升，以及模型推理延迟2.1倍的降低。