Differences

This shows you the differences between two versions of the page.

--- education [2020/11/24 12:03]
fablpd
+++ education [2024/05/16 16:26]
fablpd
@@ Line 8: / Line 8: @@
 \\
-  * [[education/ca_2020|Concurrent Algorithms]] (theory & practice)
+  * [[education/ca_2023|Concurrent Algorithms]] (theory & practice)
-  * [[education/da|Distributed Algorithms]] (theory & practice)
+  * [[education/da_2023|Distributed Algorithms]] (theory & practice)
 \\
 The lab taught in the past the following courses:
@@ Line 28: / Line 28: @@
   * **[[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.
+  * **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 [ [[https://papers.nips.cc/paper/2017/hash/f4b9ec30ad9f68f89b29639786cb62ef-Abstract.html|1]], [[https://proceedings.mlr.press/v162/farhadkhani22a.html|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  [ [[https://proceedings.neurips.cc/paper/2021/hash/d2cd33e9c0236a8c2d8bd3fa91ad3acf-Abstract.html|3]], [[https://openreview.net/forum?id=jXKKDEi5vJt|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 [[https://people.epfl.ch/youssef.allouah?lang=en|Youssef Allouah]] for more information.
-  * **Making Blockchain Accountable**: Abstract: One of the key drawback of blockchain is its lack of accountability. In fact, it does not hold participants responsible for their actions. This is easy to see as a malicious or Byzantine user typically double spends in a branch of blocks that disappears from the system, hence remaining undetected. Accountability is often thought to be communication costly: to detect a malicious participants who has sent deceitful messages to different honest participants for them to disagree, one may be tempted to force each honest participant to exchange all the messages they receive and cross-check them. However, we have recently designed an algorithm that shares the same communication complexity as the current consensus algorithms of existing blockchains. The goal of this project is to make blockchains accountable by implementing this accountable consensus algorithm and comparing it on a distributed set of machines against a baseline implementation. Contact [[https://people.epfl.ch/vincent.gramoli|Vincent Gramoli]] for more information.
+  * **Benchmark to certify Byzantine-robustness in ML**: Context: Multiple attacks have been proposed to instantiate a Byzantine adversary in distributed ML [ [[https://proceedings.neurips.cc/paper/2019/hash/ec1c59141046cd1866bbbcdfb6ae31d4-Abstract.html|1]], [[https://proceedings.mlr.press/v115/xie20a.html|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 [[https://people.epfl.ch/youssef.allouah?lang=en|Youssef Allouah]] for more information.
-  * **GAR performances on different datasets**: Robust machine learning on textual data and content recommendation is critical for the safety of social media users (harassment, hate speech, etc.), but also for the reliability of scientific use of natural language processing such for processing computer programs, chemistry and drug discovery. Text datasets are known to have long-tailed distributions, which poses specific challenges for robustness, while content recommendation datasets may feature clusters of similar users. The goal of this project is to better understand the properties of different datasets, and what makes a gradient aggregation rule (e.g. Krum, trimmed mean...) better than another, given a specific text dataset (conversational chatbots, translation, github code etc.). Contact [[https://people.epfl.ch/le.hoang|Lê Nguyên Hoang]]  for more information.
-  * **Strategyproof collaborative filtering**: In collaborative filtering, other users' inputs are used to generalize the preferences of a given user. Such an approach has been critical to improve performance. However, it exposes each user to being manipulated by the inputs of malicious users, which is arguably currently occurring on social medias. In this theoretical project, we search for Byzantine-resilient and strategyproof learning algorithms to perform something akin to collaborative filtering. This would also have important applications for implicit voting systems on exponential-size decision sets. Contact [[https://people.epfl.ch/le.hoang|Lê Nguyên Hoang]] 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.
+  * **Evaluating Distributed Systems**: By nature, distributed systems are hard to evaluate. Deploying real world systems and orchestrating large scale experiments require dedicated software and expensive infrastructure. As a result, many widespread distributed systems are not properly evaluated, tested on uncomparable or irreproductible setups. Projects of this category aim to build efficient and scalable evaluation tools for distributed systems. Diablo-related projects involve building a test harness for evaluating blockchains (skills required: network programming, blockchain, Go, C++). Another set of projects focus on creating large networks simulators able to emulate hundreds of powerful machines from a single physical server (skills required: system programming, virtualization, C, C++). Contact [[https://people.epfl.ch/gauthier.voron/?lang=en|Gauthier Voron]] for more information.
+  * **Smart Contracts and Decentralized Software**: Smart contracts are one of the key innovations brought by blockchains, enabling users to deploy codes that get executed transparently, autonomously and in a decentralized fashion. However, the applicability of smart contracts is hampered by their limited performance. Projects of this category aim to build runtime environments for fast and efficient execution of smart contracts. The first set of projects address the challenge of deterministic parallelism, or how to use several threads to
+execute a smart contract while guaranteeing a deterministic result (skills required: compiler principles, Rust). The second set of projects explores the concept of non-transactional smart contracts, a way to remove the notion of gas in smart contracts (skills required: system programming, C, Rust). The last set of projects focus on high-throughput cryptographic primitives: how to use hardware acceleration to speed up transaction authentication (skills required: cryptography principles, GPU programming, C, Assembly). Contact [[https://people.epfl.ch/gauthier.voron/?lang=en|Gauthier Voron]] 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.
-  * **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.
+  * **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 [[https://arxiv.org/abs/2008.00742|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 [[https://people.epfl.ch/sadegh.farhadkhani?lang=en|Sadegh Farhadkhani]] 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.
+  * **Accelerate Byzantine collaborative learning**: [[https://arxiv.org/abs/2008.00742|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 [[https://people.epfl.ch/sadegh.farhadkhani?lang=en|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@epfl.ch|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 [[https://people.epfl.ch/athanasios.xygkis|Athanasios Xygkis]] and [[https://people.epfl.ch/antoine.murat|Antoine Murat]] for more information.
 \\
-  * **Theory of evolution to improve GAN training**: Generative adversarial networks (GANs) have achieved some spectacular results in the six years since they have been introduced. However, their training process is still fraught with issues such as mode collapse, non-convegence or gradients collapse. Those issues have still not been completely resolved, making multiple restarts and selection of well-performing Generator-Discriminator pairs part of GAN training process. The process of adversarial generator-discriminator training is not dissimilar from co-evolution of two adversarial species - such as for instance hosts and pathogens, except that rounds of mutation/recombination/selection in search of fitness optimum are replaced by gradient descent. Our goal is to investigate - both experimentally and theoretically - if we can further stabilize and improve GAN training with evolutionary mechanisms, such as speciation, aneuploidization, neutral variability buffering or meta-evolutionary mechanisms. This work wold have implications on developing efficient solutions to detecting GAN products (aka deepfakes). You will need to have experience with scientific computing in Python, ideally with PyTorch experience, and ideally you should have some knowledge of population genetics.  Contact [[https://people.epfl.ch/andrei.kucharavy|Andrei Kucharavy]] for more information.
-===== Semester Projects =====
-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 [[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.
+===== Semester Projects =====
-===== Collaborative Projects =====
+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.
-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:
+EPFL I&C duration, credits and workload information are available on [[https://www.epfl.ch/schools/ic/education/master/semester-project-msc/|https://www.epfl.ch/schools/ic/education/master/semester-project-msc/]].
-  - **[[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.