7 papers
AgroBench: A Reproducible Multimodal Benchmark for Weakly Supervised Crop Yield Learning from County Statistics and Pixel Observations
Udaiveer Singh, Rajiv Ranjan, Shashank Tamaskar +1
Reliable agricultural yield statistics are typically reported at coarse administrative scales, whereas modern geospatial machine learning methods require spatially explicit, pixel…
SPEAR NeXT Causal Latent Forecasting Across Multiple Horizons for Spectral Temporal Earth Representation Learning
Rajiv Ranjan, Udaiveer Singh, Shashank Tamaskar +1
Earth observation is inherently dynamic, yet temporal information in many foundation models is learned through reconstruction, invariance, or retrospective sequence summarization.…
Physically Typed and Geometry-Aware Representations for Earth Foundation Models
Rajiv Ranjan
Earth-observation (EO) foundation models have become exceptionally effective at learning se mantic, high-dimensional geospatial embeddings, while modern weather and climate models…
Adaptive Joint Compression and Synchronisation in Federated Split Learning for IoT Rainfall Prediction
Wenjie Ding, Yi Sin Lin, Jiale Liu +8
Federated split learning (FSL) enables collaborative training across bandwidth-constrained IoT devices, but repeated activation and gradient exchange creates a communication bot-tl…
Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda
Minxian Xu, Jingfeng Wu, Shengye Song +16
The rapid rise of Large Language Models (LLMs) has revolutionized various artificial intelligence (AI) applications, from natural language processing to code generation. However, t…
D2R: dual regularization loss with collaborative adversarial generation for model robustness
Zhenyu Liu, Huizhi Liang, Rajiv Ranjan +3
The robustness of Deep Neural Network models is crucial for defending models against adversarial attacks. Recent defense methods have employed collaborative learning frameworks to…
Security Assessment of Hierarchical Federated Deep Learning
D Alqattan, R Sun, H Liang +4
Hierarchical federated learning (HFL) is a promising distributed deep learning model training paradigm, but it has crucial security concerns arising from adversarial attacks. This…