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 ====== Education ======
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 The lab is teaching the following courses:
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+  * [[education/ca_2023|Concurrent Algorithms]] (theory & practice)
+  * [[education/da_2023|Distributed Algorithms]] (theory & practice)
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-  * [[education/ca_2018|Concurrent Algorithms]]
+The lab taught in the past the following courses:
-  * [[education/da|Distributed Algorithms]]
   * <html><a href="http://moodle.epfl.ch/course/view.php?id=14044">Information, Calcul et Communication</a></html>
   * <html><a href="http://cowww.epfl.ch/proginfo/wwwhiver/">Introduction à la Programmation Orientée Objet</a></html>
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 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@epfl.ch|Matteo Monti]] to get more information.
+  * **[[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]].
-  * **Dynamically Distributed Spatial Indexing**:  a project here would consist in studying existing spatial index data structures and algorithms, e.g., simple grids, Quadtrees, R-Trees etc., and how they may be dynamically distributed for indexing a large number of moving objects; please contact [[mailto:benoit.garbinato@unil.ch|Benoit Garbinato]] to get 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.
+  * **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.
-  * **Multicore computing**: a project here would consist for instance in designing and implementing efficient lock-based or lock-free shared objects; please contact [[https://people.epfl.ch/igor.zablotchi|Igor Zablotchi]] to get more information.
-  * **Distributed computing using RDMA and/or NVRAM**: contact [[https://people.epfl.ch/igor.zablotchi|Igor Zablotchi]] for more information.
-  * **[[Distributed ML|Distributed Machine Learning]]**
+  * **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. [[https://dl.acm.org/doi/10.1145/3552326.3567482|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.
-  * **Distributed and Fault-tolerant algorithms**: projects here would consist in designing failure detection mechanisms suited for large-scale systems, real-time systems, and systems with unreliable communication or partial synchrony. This task also involves implementing, evaluating, and simulating the performance of the developed mechanisms to verify the achievable guarantees; please contact [[http://people.epfl.ch/david.kozhaya|David Kozhaya]] to get 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.
-  * **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]] for further information.
+  * **Safe and Scalable Consensus**: Decentralized systems like cryptocurrencies rely on the concept of consensus. This component is critical as it dictates how performant, safe and scalable a distributed system is. Over the last years, the DCL has pushed the performance of consensus algorithms to [[https://arxiv.org/pdf/2304.07081|unprecedented levels]] but the practical safety and scalability are yet to be addressed. Projects of this category focus on designing and implementing distributed consensus algorithms which are safer against cyberattacks or adverse environments and work with higher number of participants. On one side, some projects explore new **consensus designs** with good theoretical guarantees and practical behaviors (skills required: distributed algorithms, network programming, Go). On the other side, some projects focus on ensuring the correctness of existing consensus algorithms through **model checking** at various levels (skills required: distributed algorithms, Rust, TLA+). Contact [[https://people.epfl.ch/gauthier.voron/?lang=en|Gauthier Voron]] for more information.
-  * **Distributed database algorithms**: a project here would consist in implementing and evaluating protocols that are running in today's database systems, e.g., [[https://en.wikipedia.org/wiki/Two-phase_commit_protocol|2PC]], and comparing them with those protocols that can  potentially be used in future database systems; please contact [[http://people.epfl.ch/jingjing.wang|Jingjing Wang]] to get more information.
+  * **Certified Machine Learning**: Machine learning techniques have developed rapidly in recent years, with impressive results and widespread adoption. However, many models are closed and executed on remote servers belonging to private companies. Moreover, the training process of these models remain obscure, pushing public institutions to look forward auditable and certified machine learning in the hope of better regulation of this industry. Projects on this category aim to build systems that make possible to create and use **certified machine learning** models (skills required: principles of machine learning, PyTorch, Go). Contact [[https://people.epfl.ch/gauthier.voron/?lang=en|Gauthier Voron]] or [[https://people.epfl.ch/Geovani.Rizk/?lang=en|Geovani Rizk]] 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.
+  * **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.
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 ===== Semester Projects =====
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 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.
+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/]].