The lab is teaching the following courses:
The lab taught in the past the following courses:
DCL offers master projects in the following areas:
- Tackling data heterogeneity in Byzantine-robust ML: Context: Distributed ML is a very effective paradigm to learn collaboratively when all users correctly follow the protocol. However, some users may behave adversarially and measures should be taken to protect against such Byzantine behavior [ 1, 2 ]. In real-world settings, users have different datasets (i.e. non-iid), which makes defending against Byzantine behavior challenging, as was shown recently in [ 3, 4 ]. Some defenses were proposed to tackle data heterogeneity, but their performance is suboptimal on simple learning tasks. Goal: Develop defenses with special emphasis on empirical performance and efficiency in the heterogeneous setting. Contact Youssef Allouah for more information.
- Benchmark to certify Byzantine-robustness in ML: Context: Multiple attacks have been proposed to instantiate a Byzantine adversary in distributed ML [ 1, 2 ]. While these attacks have been successful against known defenses, it remains unknown whether stronger attacks exist. As such, a strong benchmark is needed, to go beyond the cat-and-mouse game illustrating the existing research. Ideally, similar to other ML subfields such as privacy-preserving ML or adversarial examples, the desired benchmark should guarantee that no stronger attack exists. Goal: Develop a strong benchmark for attacks in Byzantine ML. Contact Youssef Allouah for more information.
- Topology-aware mempool for cryptocurrencies: The mempool is a core component of cryptocurrency systems. It disseminates user transactions to the miner nodes before they reach consensus.Current mempools assume an homogeneous network topology where all machines have the same bandwidth and latency.This unrealitic assumption forces the system to progress at the same speed as the slowest node in the system. This project aims at implementing a mempool which exploits the heterogeneity of the network to speed up data dissemination for cryptocurrency systems. This is a practical project which requires good knowledge in network programming, either Go or C++, distributed algorithms. Contact Gauthier Voron email@example.com for more information.
- Robust mean estimation: In recent years, many algorithms have been proposed to perform robust mean estimation, which has been shown to be equivalent to robust gradient-based machine learning. A new concept has been proposed to define the performance of a robust mean estimator, called the averaging constant (along with the Byzantine resilience). This research project consists of computing the theoretical averaging constant of different proposed robust mean estimators, and to study their empirical performances on randomly generated vectors. Contact Sadegh Farhadkhani for more information.
- Accelerate Byzantine collaborative learning: Our recent NeurIPS paper proposed algorithms for collaborative machine learning in the presence of Byzantine nodes, which have been proved to be near optimal with respect to optimality at convergence. However, these algorithms require all-to-all communication at every round, which is suboptimal. This research consists of designing a practical solution to Byzantine collaborative learning, based on the idea of a random communication network at each round, with both theoretical guarantees and practical implementation. Contact Sadegh Farhadkhani for more information.
- Probabilistic Byzantine Resilience: Development of high-performance, Byzantine-resilient distributed systems with provable probabilistic guarantees. Two options are currently available, both building on previous work on probabilistic Byzantine broadcast: (i) a theoretical project, focused the correctness of probabilistic Byzantine-tolerant distributed algorithms; (ii) a practical project, focused on numerically evaluating of our theoretical results. Please contact Matteo Monti to get more information.
- Microsecond-scale dependable systems. Modern networking technologies such as RDMA (Remote Direct Memory Access) allow for sub-microsecond communication latency. Combined with emerging data center architectures, such as disaggregated resources pools, they open the door to novel blazing-fast and resource-efficient systems. Our research focuses on designing such microsecond-scale systems that can also tolerate faults. Our vision is that tolerating network asynchrony as well as faults (crash and/or Byzantine) is a must, but that it shouldn't affect the overall performance of a system. We achieve this goal by devising and implementing novel algorithms tailored for new hardware and revisiting theoretical models to better reflect modern data centers. Previous work encompasses microsecond-scale (BFT) State Machine Replication, Group Membership Services and Key-Value Stores (OSDI'20, ATC'22 and ASPLOS'23). Overall, if you are interested in making data centers faster and safer, contact Athanasios Xygkis and Antoine Murat for more information.
If the subject of a Master Project interests you as a Semester Project, please contact the supervisor of the Master Project to see if it can be considered for a Semester Project.
EPFL I&C duration, credits and workload information are available on https://www.epfl.ch/schools/ic/education/master/semester-project-msc/.