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education [2019/06/17 15:45]
zablotch
education [2019/11/08 11:02] (current)
rouault Removed "variance reduction adversarial SGD" project
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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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   * **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.   * **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.
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-  * **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. 
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   * **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.