Zhifeng Jiang @ HKUST 江志锋

Pisces: Efficient Federated Learning via Guided Asynchronous Training

Zhifeng Jiang, Wei Wang, Baochun Li, Bo Li
In the proceedings of ACM Symposium on Cloud Computing (SoCC) (2022) 

Paper Code HTML Slides Poster

We present an asynchronous FL system for accelerated training. To avoid incurring excessive resource cost and stale training computation, we use a novel scoring mechanism to select participants. We also adapt the pace of model aggregation to dynamically bound the progress gap between the selected clients and the server.

---

Gillis: Serving Large Neural Networks in Serverless Functions with Automatic Model Partitioning

Minchen Yu, Zhifeng Jiang, Hok Chun Ng, Wei Wang, Ruichuan Chen, Bo Li
IEEE International Conference on Distributed Computing Systems (ICDCS) (2021) 

Best Paper Runner-Up

Paper Code HTML Recording

We present a serverless-based model serving system that automatically partitions a large model across multiple serverless functions. It employs two novel model partitioning algorithms that respectively achieve latency-optimal serving and cost-optimal serving with SLO compliance.

---

Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies

Zhifeng Jiang, Wei Wang, Bo Li, Qiang Yang
IEEE Transactions on Big Data (TBD) (2022) 

Paper HTML

We survey recent works in addressing the challenges emerging from synchronous FL training and present them following a typical training workflow through three phases: client selection, configuration, and reporting. We also review measurement studies and benchmarking tools.

---

Taming Client Dropout and Improving Efficiency for Distributed Differential Privacy in Federated Learning

Zhifeng Jiang, Wei Wang, Ruichuan Chen

Paper Slides Code

We present a distributed differentially private FL framework. It uses a novel 'add-then-remove' scheme where a required noise level can be enforced in each FL training round even though some sampled clients may drop out in the end. It also runs as a distributed pipeline architecture that optimally pipelines communication and computation.

---

Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learning

Peng Ye, Zhifeng Jiang, Wei Wang, Bo Li, Baochun Li

Paper Code

We focus on client data with binary features, and show that unless the feature space is exceedingly large, we can precisely reconstruct the binary features in practice with a robust search-based attack algorithm. We also present a defense mechanism that overcomes such binary feature vulnerabilities by misleading the adversary to search for fabricated features.

---

FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning

Zhifeng Jiang, Wei Wang, Yang Liu

Paper Code

We propose a homomorphic encryption scheme for efficient cross-silo FL. Compared to commonly used HE schemes, we drop the asymmetric-key design and only involves modular addition operations with random numbers. The computation efficiency is further optimized when being combined with sparsification techniques.

---

CSDrop: Leveraging Context-Sensitive Decoding to Thwart Return-Oriented Programming

Zhifeng Jiang, Mohammadkazem Taram, Ashish Venkat, Dean M. Tullsen

Report

To thwart Return-Oriented Programming attacks with microarchitecture-level modification, we propose a CPU plugin that enables context-sensitive decoding for securely backing up and validating return addresses. We evaluate its defense capability and runtime efficiency in the gem5 simulator.

---