21 citations · 34 across the 8 of their papers we have counts for
8 papers
POPri: Private Federated Learning using Preference-Optimized Synthetic Data
Charlie Hou, Mei-Yu Wang, Yige Zhu +2
In practical settings, differentially private Federated learning (DP-FL) is the dominant method for training models from private, on-device client data. Recent work has suggested t…
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Charlie Hou, Akshat Shrivastava, Hongyuan Zhan +5
On-device training is currently the most common approach for training machine learning (ML) models on private, distributed user data. Despite this, on-device training has several d…
Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion
Shuqi Ke, Charlie Hou, Sewoong Oh +1
We show that differentially private full fine-tuning (DP-FFT) can distort pre-trained backbone features based on both theoretical and empirical results. We identify the cause of th…
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
Charlie Hou, Kiran Koshy Thekumparampil, Michael Shavlovsky +3
On tabular data, a significant body of literature has shown that current deep learning (DL) models perform at best similarly to Gradient Boosted Decision Trees (GBDTs), while signi…
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning
Charlie Hou, Hongyuan Zhan, Akshat Shrivastava +4
In Federated Learning (FL), accessing private client data incurs communication and privacy costs. As a result, FL deployments commonly prefinetune pretrained foundation models on a…
FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning
Charlie Hou, Kiran K. Thekumparampil, Giulia Fanti +1
Federated learning (FL) aims to minimize the communication complexity of training a model over heterogeneous data distributed across many clients. A common approach is local method…
Efficient Algorithms for Federated Saddle Point Optimization
Charlie Hou, Kiran K. Thekumparampil, Giulia Fanti +1
We consider strongly convex-concave minimax problems in the federated setting, where the communication constraint is the main bottleneck. When clients are arbitrarily heterogeneous…
SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning
Charlie Hou, Mingxun Zhou, Yan Ji +4
Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing in…