Jannatul Ferdaus

Sai Puppala, Ismail Hossain, Md Jahangir Alam et al.

Federated learning has become a significant approach for training machine learning models using decentralized data without necessitating the sharing of this data. Recently, the incorporation of generative artificial intelligence (AI) methods has provided new possibilities for improving privacy, augmenting data, and customizing models. This research explores potential integrations of generative AI in federated learning, revealing various opportunities to enhance privacy, data efficiency, and model performance. It particularly emphasizes the importance of generative models like generative adversarial networks (GANs) and variational autoencoders (VAEs) in creating synthetic data that replicates the distribution of real data. Generating synthetic data helps federated learning address challenges related to limited data availability and supports robust model development. Additionally, we examine various applications of generative AI in federated learning that enable more personalized solutions.

Jannatul Ferdaus, Sameera Pisupati, Mahedi Hasan et al.

Federated Learning enables robots to learn from each other's experiences without relying on centralized data collection. Each robot independently maintains a model of crop conditions and chemical spray effectiveness, which is periodically shared with other robots in the fleet. A communication protocol is designed to optimize chemical spray applications by facilitating the exchange of information about crop conditions, weather, and other critical factors. The federated learning algorithm leverages this shared data to continuously refine the chemical spray strategy, reducing waste and improving crop yields. This approach has the potential to revolutionize the agriculture industry by offering a scalable and efficient solution for crop protection. However, significant challenges remain, including the development of a secure and robust communication protocol, the design of a federated learning algorithm that effectively integrates data from multiple sources, and ensuring the safety and reliability of autonomous robots. The proposed cluster-based federated learning approach also effectively reduces the computational load on the global server and minimizes communication overhead among clients.

Ismail Hossain, Sai Puppala, Jannatul Ferdaus et al.

A guard model fine-tuned on entirely benign data can lose all safety alignment -- not through adversarial manipulation, but through standard domain specialization. We demonstrate this failure across three purpose-built safety classifiers -- LlamaGuard, WildGuard, and Granite Guardian -- deployed as protection layers in agentic AI pipelines, and show that it originates in the destruction of latent safety geometry: the structured harmful -- benign representational boundary that guides classification. We extract per-layer safety subspaces via SVD on class-conditional activation differences and track how this boundary evolves under benign fine-tuning. Granite Guardian undergoes complete collapse -- refusal rate drops from 85\% to 0\%, CKA falls to zero, and 100\% of outputs become ambiguous -- a severity exceeding prior findings on general-purpose LLMs, explained by the specialization hypothesis: concentrated safety representations are efficient but catastrophically brittle. To mitigate this, we propose Fisher-Weighted Safety Subspace Regularization (FW-SSR), a training-time penalty combining (i) curvature-aware direction weights derived from diagonal Fisher information and (ii) an adaptive $λ_t$ that scales with task-safety gradient conflict. FW-SSR recovers 75\% refusal on Granite Guardian (CKA = 0.983) and reduces WildGuard's Attack Success Rate to 3.6\% -- below the unmodified baseline -- by actively sharpening the safety subspace rather than merely anchoring it. Across all three models, structural representational geometry (CKA, Fisher score) predicts safety behavior more reliably than absolute displacement metrics, establishing geometry-based monitoring as a necessary component of guard model evaluation in agentic deployments.