1 citations · 1 across the 4 of their papers we have counts for
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ProToken: Token-Level Attribution for Federated Large Language Models
Waris Gill, Ahmad Humayun, Ali Anwar +1
Federated Learning (FL) enables collaborative training of Large Language Models (LLMs) across distributed data sources while preserving privacy. However, when federated LLMs are de…
Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data
Waris Gill, Justin Cechmanek, Tyler Hutcherson +5
This report investigates enhancing semantic caching effectiveness by employing specialized, fine-tuned embedding models. Semantic caching relies on embedding similarity rather than…
MeanCache: User-Centric Semantic Caching for LLM Web Services
Waris Gill, Mohamed Elidrisi, Pallavi Kalapatapu +3
Large Language Models (LLMs) like ChatGPT and Llama have revolutionized natural language processing and search engine dynamics. However, these models incur exceptionally high compu…
TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
Waris Gill, Ali Anwar, Muhammad Ali Gulzar
In Federated Learning, clients train models on local data and send updates to a central server, which aggregates them into a global model using a fusion algorithm. This collaborati…