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education [2019/06/02 15:04]
seredins
education [2020/07/04 22:27]
guirguis
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-  * [[education/​ca_2018|Concurrent Algorithms]] (theory & practice)+  * [[education/​ca_2019|Concurrent Algorithms]] (theory & practice)
   * [[education/​da|Distributed Algorithms]] (theory & practice)   * [[education/​da|Distributed Algorithms]] (theory & practice)
 \\ \\
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   * **[[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]].   * **[[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]].
 +
 +  * **On the design and implementation of scalable and secure blockchain algorithms**:​ Consensus has recently gained in popularity with the advent of blockchain technologies. Unfortunately,​ most blockchains do not scale due, in part, to their centralized (leader-based) limitation. We recently designed a promising fully decentralised (leader-less) algorithm that promises to scale to large networks. The goal of this project is to implement it in rust and compare its performance on AWS instances against a traditional leader-based alternative like BFT-Smart whose code will be provided. Contact [[https://​people.epfl.ch/​vincent.gramoli|Vincent Gramoli]] 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@epfl.ch|Matteo Monti]] to get 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@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 Accessallows accessing a remote machine'​s memory without interrupting its CPUNVRAM is byte-addressable persistent ​(non-volatilememory with access times on the same order of magnitude as traditional ​(volatileRAMThese 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]]**:​ contact [[http://​people.epfl.ch/​georgios.damaskinos|Georgios Damaskinos]] 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 Byzantineaffecting the model convergence in the learning phaseOur 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. +
- +
-  * **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 workersrelies ​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 (drasticallyincrease 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 distributionThe 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.+
  
 +  * **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://​mlsys.org/​Conferences/​2019/​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|Adi Seredinschi]] or [[https://​people.epfl.ch/​karolos.antoniadis|Karolos Antoniadis]] ​ for further 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. 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 [[http://​lara.epfl.ch|LARA]] 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.
 +  - **[[https://​github.com/​viperproject/​prusti-dev|Prusti]]:​** Implementation of Tendermint modules (consensus, mempool, fast sync) using Prusti and the Rust programming language.
 +  - **[[https://​tendermint.com/​docs/​spec/​reactors/​mempool/​functionality.html#​mempool-functionality|Mempool]]** performance analysis and algorithm improvement.
 +  - **Adversarial engineering:​** Experimental evaluation of Tendermint in adversarial settings (e.g., in the style of [[http://​jepsen.io/​analyses/​tendermint-0-10-2|Jepsen]]).
 +  - **Testing**:​ Generation of tests out of specifications (TLA+ or Stainless) for the consensus module of Tendermint.
 +  - **Facebook Libra comparative research**: Comparative analysis of consensus algorithms, specifically,​ between HotStuff (the consensus algorithm underlying [[https://​cryptorating.eu/​whitepapers/​Libra/​libra-consensus-state-machine-replication-in-the-libra-blockchain.pdf|Facebook'​s Libra]]) and Tendermint consensus.
 +
 +Contact [[adi@interchain.io|Adi Seredinschi]] (INR 327) if interested in learning more about these projects.