4 papers
Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries
Hina Dixit, Punit Kumar, Irene Tenison +1
Cross-organization language-model adaptation increasingly faces hard governance constraints: in many deployments, device-level model state-parameters, activations, optimizer state,…
Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation
Irene Tenison, Stella Ahn, Miriam Kim +2
Parameter-Efficient Fine-Tuning (PEFT) has become the standard for adapting large language models (LLMs). In this work we challenge the wide-spread assumption that parameter effici…
FTTE: Enabling Federated and Resource-Constrained Deep Edge Intelligence
Irene Tenison, Anna Murphy, Charles Beauville +1
Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy, but deployment on resource-constrained edge nodes remains cha…
Forget to Generalize: Iterative Adaptation for Generalization in Federated Learning
Abdulrahman Alotaibi, Irene Tenison, Miriam Kim +2
The Web is naturally heterogeneous with user devices, geographic regions, browsing patterns, and contexts all leading to highly diverse, unique datasets. Federated Learning (FL) is…