Publications (6)
PoTo: A Hybrid Andersen's Points-to Analysis for Python
Ingkarat Rak-amnouykit, Ana Milanova, Guillaume Baudart +2
As Python is increasingly being adopted for large and complex programs, the importance of static analysis for Python (such as type inference) grows. Unfortunately, static analysis…
Improving Parameter-Efficient Federated Learning with Differentially Private Refactorization
Linh Tran, Ana Milanova, Stacy Patterson
Federated Learning (FL) with parameter-efficient fine-tuning, such as Low-Rank Adaptation (LoRA), enables scalable model training on distributed data. However, when combined with D…
Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models
Linh Tran, Wei Sun, Stacy Patterson +1
Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated…
PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy
Linh Tran, Timothy Castiglia, Stacy Patterson +1
We present Poisson Binomial Mechanism Vertical Federated Learning (PBM-VFL), a communication-efficient Vertical Federated Learning algorithm with Differential Privacy guarantees. P…
FlowCFL: A Framework for Type-based Reachability Analysis in the Presence of Mutable Data
Ana Milanova
Reachability analysis is a fundamental program analysis with a wide variety of applications. We present FlowCFL, a framework for type-based reachability analysis in the presence of…
Formalizing Event-Driven Behavior of Serverless Applications
Matthew Obetz, Stacy Patterson, Ana Milanova
We present new operational semantics for serverless computing that model the event-driven relationships between serverless functions, as well as their interaction with platforms se…