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-  * [[education/ca_2018|Concurrent Algorithms]] (theory & practice)
+  * [[education/ca_2021|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]].
+  * **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.
+  * **Distributed coordination using RDMA.** RDMA (Remote Direct Memory Access) allows accessing a remote machine's memory without interrupting its CPU. This technology is gaining traction over the last couple of years, as it allows for the creation of real-time distributed systems. RDMA allows for communication to take place close to the μsec scale, which enables the design and implementation of systems that process requests in only tens of μsec. Current research focuses on achieving real-time failure detection through a combination of novel algorithm design, latest hardware and linux kernel customization. Fast failure detection over RDMA brings the notion of availability to a new level, essentially allowing modern systems to enter the era of 7 nines of availability. Contact [[https://people.epfl.ch/athanasios.xygkis|Athanasios Xygkis]] and [[https://people.epfl.ch/antoine.murat|Antoine Murat]] for more information.
-  * **Distributed computing using RDMA and/or NVRAM**: contact [[https://people.epfl.ch/igor.zablotchi|Igor Zablotchi]] 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.
-  * **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.
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+  * **Byzantine-resilient heterogeneous GANs**: Byzantine-resilient federated learning has emerged as a major theme over the last couple of years, in grand part due to the need to distribute machine learning across many nodes, due to performance and privacy concerns. Until now it has focused on training a single model across many workers and many parameter serves. While this approach has brought on formidable results - including in GAN training, the topic of efficient, distributed and byzantine-resilient training of heterogeneous architectures remain relatively unexplored. In the context of Generative adversarial networks (GANs), such learning is critical to training light discriminators that can specialize in detecting specific featuers of generator-generated images. The goal of this project will be to investigate the potential for GAN training process poisonning by malicious discriminators and generators and investigate efficient protocols to ensure the training process robustness. You will need to have experience with scientific computing in Python, ideally with PyTorch experience, and notions of distributed computing. Contact [[https://people.epfl.ch/andrei.kucharavy|Andrei Kucharavy]] for more information.
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+  * **Hijacking proof-of-work to make it useful: distributed gradient-free learning approach**: Proof-of-work blockchains - notably Bitcoin and Ethereum - reach a probabilistic consensus about the contents of the blockchain by a mechanism of probabilistic leader election. Every contributor to the consensus tries to solve a puzzle, and the first one to succeed is elected a leader, allowed to create the next block and publicly add information to it. The puzzle needs to be hard to solve and easy to verify, solvable only by random guessing and not allowing for any shortcuts and allow for its difficulty to be tuned so that nodes don't find answers to it simultaneously and take different leaderships forking the chain in two. Partial cryptographic hash reversal has traditionally been a perfect candidate for such puzzle, but it has no interest outside being a challenge for blockchain. And with 100-300 PetaFLOP/s (drawing 100 TWh/y) of general purpose computational power being tied into Ethereum blockchain alone as of early 2022, the waste of computational resources and energy is colossal. While the interest of blockchains and the suitability of proof-of-work as a mechanism to run them is widely debated, it's at this day the mechanism for the two largest ones. We try to at least use some of that challenge useful by injecting a "try" step of a (1,λ)-ES evolutionary search algorithm into the hash computation loop, slowing it down and making it do something useful in during the slowdown period. This class of evolutionary search algorithm achieves a good performance on black-bock optimization tasks (sometimes exceeding RL approaches in traditionally RL problems), is embarrassingly parallel, fits well the requirements for a proof-of-work function and can be empirically optimized to minimize the waste of computational resources during a training run. However, in its current state the (1,λ)-ES-based useful proof-of-work has been proven to work in cases where the data used for the training tasks can be fully replicated among the nodes. For numerous applications, it is not an option. Finding ways to solve that problem, both from a theoretical and an experimental perspective will be the goal of this project. You will need solid skills in Python (Rust and WebAssembly are a plus), basic understanding of distributed algorithms and of machine learning concepts. Some familiarity with blockchains and black box optimization is a plus, but is not a requirement. Contact [[https://people.epfl.ch/andrei.kucharavy|Andrei Kucharavy]] for more information.
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 ===== Semester Projects =====
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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.