Differences

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

Link to this comparison view

Both sides previous revision Previous revision
Next revision Both sides next revision
education [2020/10/29 18:19]
fablpd 		education [2020/10/29 18:21]
fablpd
Line 29: Line 29:
  
   * **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.   * **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.
 +
 +  * **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.
  
   * **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.   * **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.