Recent advancements in foundation models have improved autonomous tool usage and reasoning, but their capabilities in map-based reasoning remain underexplored. To address this, we introduce MapEval, a benchmark designed to assess foundation models across three distinct tasks—textual, API-based, and visual reasoning—through 700 multiple-choice questions spanning 180 cities and 54 countries, covering spatial relationships, navigation, travel planning, and real-world map interactions. Unlike prior benchmarks that focus on simple location queries, MapEval requires models to handle long-context reasoning, API interactions and visual map analysis, making it the most comprehensive evaluation framework for geospatial AI. On evaluation of 30 foundation models, including Claude-3.5-Sonnet, GPT-4o, Gemini-1.5-Pro, none surpasses 67% accuracy, with open-source models performing significantly worse and all models lagging over 20% behind human performance. These results expose critical gaps in spatial inference, as models struggle with distances, directions, route planning, and place-specific reasoning, highlighting the need for better geospatial AI to bridge the gap between foundation models and real-world navigation.

Federated machine learning (FL) is susceptible to poisoning attacks, where malicious clients embed backdoors into the global model. Even a single attacker can launch potent attacks, challenging existing defenses. This work proposes GuardFL, a novel defense against backdoor attacks in FL. GuardFL integrates majority consensus and client feedback mechanisms to detect and isolate malicious clients. Extensive testing demonstrates the effectiveness of GuardFL in identifying and isolating attackers. We evaluate GuardFL using three benchmark image classification datasets including CIFAR-10, MNIST, and FMNIST (Fashion MNIST). We believe our techniques yield a robust defense against backdoor poisoning in FL.