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The lab is teaching the following courses:
The lab taught in the past the following courses:
DCL offers master projects in the following areas:
- 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.
- Distributed computing using RDMA and/or NVRAM. RDMA (Remote Direct Memory Access) allows accessing a remote machine's memory without interrupting its CPU. NVRAM is byte-addressable persistent (non-volatile) memory with access times on the same order of magnitude as traditional (volatile) RAM. These two recent technologies pose novel challenges and raise new opportunities in distributed system design and implementation. Contact Igor Zablotchi for more information.
- Robust Distributed Machine Learning: With the proliferation of big datasets and models, Machine Learning is becoming distributed. Following the standard parameter server model, the learning phase is taken by two categories of machines: parameter servers and workers. Any of these machines could behave arbitrarily (i.e., said Byzantine) affecting the model convergence in the learning phase. Our goal in this project is to build a system that is robust against Byzantine behavior of both parameter server and workers. Our first prototype, AggregaThor(https://www.sysml.cc/doc/2019/54.pdf), describes the first scalable robust Machine Learning framework. It fixed a severe vulnerability in TensorFlow and it showed how to make TensorFlow even faster, while robust. Contact Arsany Guirguis for more information.
- Stochastic gradient: (artificial) reduction of the ratio variance/norm for adversarial distributed SGD: One computationally-efficient and non-intrusive line of defense for adversarial distributed SGD (e.g. 1 parameter server distributing the gradient estimation to several, possibly adversarial workers) relies on the honest workers to send back gradient estimations with sufficiently low variance; assumption which is sometimes hard to satisfy in practice. One solution could be to (drastically) increase the batch-size at the workers, but doing so may as well defeat the very purpose of distributing the computation.
In this project, we propose two approaches that you can choose to explore (also you may propose a different approach) to (artificially) reduce the ratio variance/norm of the stochastic gradients, while keeping the benefits of the distribution. The first proposed approach, speculative, boils down to “intelligent” coordinate selection. The second makes use of some kind of “momentum” at the workers.
 "Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent"
 "Federated Learning: Strategies for Improving Communication Efficiency"
Contact Sébastien Rouault for more information.
- Consistency in global-scale storage systems: We offer several projects in the context of storage systems, ranging from implementation of social applications (similar to Retwis, or ShareJS) to recommender systems, static content storage services (à la Facebook's Haystack), or experimenting with well-known cloud serving benchmarks (such as YCSB); please contact Adi Seredinschi or Karolos Antoniadis for further 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 here. Don't hesitate to contact the project supervisor if you want to complete your Semester Project outside the regular semester period.