6 papers
Joint Continual Learning of Local Language Models and Cloud Offloading Decisions with Budget Constraints
Evan Chen, Wenzhi Fang, Shiqiang Wang +1
Locally deployed Small Language Models (SLMs) must continually support diverse tasks under strict memory and computation constraints, making selective reliance on cloud Large Langu…
Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach
Dandan Liang, Jianing Zhang, Evan Chen +3
Split Federated Learning (SFL) enables scalable training on edge devices by combining the parallelism of Federated Learning (FL) with the computational offloading of Split Learning…
Federated Foundation Models in Harsh Wireless Environments: Prospects, Challenges, and Future Directions
Evan Chen, Seyyedali Hosseinalipour, Christopher G. Brinton +1
Foundation models (FMs) have shown remarkable capabilities in generalized intelligence, multimodal understanding, and adaptive learning across a wide range of domains. However, the…
Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
Jianing Zhang, Evan Chen, Dong-Jun Han +2
Vertical Federated Learning (VFL) enables collaborative model training across feature-partitioned devices, yet its reliance on device-server information exchange introduces signifi…
Differentially-Private Multi-Tier Federated Learning: A Formal Analysis and Evaluation
Evan Chen, Frank Po-Chen Lin, Dong-Jun Han +1
While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from the communicated model parameters. Differentia…
Gradient Correction in Federated Learning with Adaptive Optimization
Evan Chen, Shiqiang Wang, Jianing Zhang +3
In federated learning (FL), model training performance is strongly impacted by data heterogeneity across clients. Client-drift compensation methods have recently emerged as a solut…