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-  * [[education/ca_2018|Concurrent Algorithms]] (theory & practice)
+  * [[education/ca_2019|Concurrent Algorithms]] (theory & practice)
   * [[education/da|Distributed Algorithms]] (theory & practice)
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 DCL offers master projects in the following areas:
+  * **[[cryptocurrencies|Cryptocurrencies]]**: We have several project openings as part of our ongoing research on designing new cryptocurrency systems. Please contact [[rachid.guerraoui@epfl.ch|Prof. Rachid Guerraoui]].
   * **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@epfl.ch|Matteo Monti]] to get more information.
-  * **Distributed computing using RDMA and/or NVRAM**: contact [[https://people.epfl.ch/igor.zablotchi|Igor Zablotchi]] for 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 [[https://people.epfl.ch/igor.zablotchi|Igor Zablotchi]] for more information.
-  * **[[Distributed ML|Distributed Machine Learning]]**
+  * **[[Distributed ML|Distributed Machine Learning]]**: contact [[http://people.epfl.ch/georgios.damaskinos|Georgios Damaskinos]] for more information.
-  * **Robust Distributed Machine Learning**: The goal of such project is to work on the design and implementation of algorithms and systems to improve the robustness of distributed ML schemes that would tolerate poisoned data, software bugs as well as hardware failures. The practical work will be done on TensorFlow or Pytorch. Contact [[https://people.epfl.ch/arsany.guirguis|Arsany Guirguis]] or [[https://people.epfl.ch/sebastien.rouault|Sébastien Rouault]] 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 [[https://people.epfl.ch/arsany.guirguis|Arsany Guirguis]] 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 [[http://retwis.redis.io/|Retwis]], or [[https://github.com/share/sharejs|ShareJS]]) to recommender systems, static content storage services (à la [[https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf|Facebook's Haystack]]), or experimenting with well-known cloud serving benchmarks (such as [[https://github.com/brianfrankcooper/YCSB|YCSB]]); please contact [[http://people.epfl.ch/dragos-adrian.seredinschi|Adrian Seredinschi]] or [[https://people.epfl.ch/karolos.antoniadis|Karolos Antoniadis]]  for further 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. \\ [1] [[https://papers.nips.cc/paper/6617-machine-learning-with-adversaries-byzantine-tolerant-gradient-descent|"Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent" ]]  \\ [2] [[https://arxiv.org/abs/1610.05492|"Federated Learning: Strategies for Improving Communication Efficiency"]] \\ Contact  [[https://people.epfl.ch/sebastien.rouault|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 [[http://retwis.redis.io/|Retwis]], or [[https://github.com/share/sharejs|ShareJS]]) to recommender systems, static content storage services (à la [[https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf|Facebook's Haystack]]), or experimenting with well-known cloud serving benchmarks (such as [[https://github.com/brianfrankcooper/YCSB|YCSB]]); please contact [[http://people.epfl.ch/dragos-adrian.seredinschi|Adi Seredinschi]] or [[https://people.epfl.ch/karolos.antoniadis|Karolos Antoniadis]]  for further information.
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 EPFL I&C duration, credits and workload information are available [[https://www.epfl.ch/schools/ic/education/|here]]. Don't hesitate to contact the project supervisor if you want to complete your Semester Project outside the regular semester period.
+===== Collaborative Projects =====
+The lab is also collaborating with the industry and other labs at EPFL to offer interesting student projects motivated from real-world problems. With [[LARA||http://lara.epfl.ch]] and [[interchain.io|Interchain Foundation]] we have several projects:
+  - **[[https://dcl.epfl.ch/site/cryptocurrencies|AT2]]:** Integration of an asynchronous (consensus-less) payment system in the Cosmos Hub.
+  - **[[https://github.com/cosmos/ics/tree/master/ibc|Interblockchain Communication (IBC)]]:** Protocols description (and optional implementation) for enabling the inter-operation of independent blockchain applications.
+- **[[http://stainless.epfl.ch|Stainless]]**: Implementation of Tendermint modules (consensus, mempool, fast sync) using Stainless and Scala.
+- **[[|Prusti]]** implementation of modules in Tendermint (consensus, mempool, fast sync).
+- Mempool performance analysis and algorithm improvement
+- Experimental evaluation of Tendermint in adversarial settings (Jepsen++)
+- Test generation out of spec (TLA+ or Stainless) for consensus module
+- Using HotStuff trick to improve Tendermint
+[[education#collaborative projects|test link]]