4 papers
AgBench: Agentic AI Benchmarks for Personal AI Devices
Yizhou Han, Di Wu, Dhananjay Saikumar +1
Agentic AI systems increasingly rely on cloud-hosted large language models for planning, tool use, and iterative execution, raising concerns about API cost and data exposure. Advan…
DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning
Yizhou Han, Di Wu, Blesson Varghese
In real-world Federated Learning (FL) deployments, data distributions on devices that participate in training evolve over time. This leads to asynchronous data drift, where differe…
EMO: Edge Model Overlays to Scale Model Size in Federated Learning
Di Wu, Weibo He, Wanglei Feng +3
Federated Learning (FL) trains machine learning models on edge devices with distributed data. However, the computational and memory limitations of these devices restrict the traini…
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
Zhenyu Wen, Wanglei Feng, Di Wu +6
Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Altho…