Zobrazeno 1 - 10
of 1 802
pro vyhledávání: '"Guerraoui, A."'
Federated learning (FL) is an appealing paradigm that allows a group of machines (a.k.a. clients) to learn collectively while keeping their data local. However, due to the heterogeneity between the clients' data distributions, the model obtained thro
Externí odkaz:
http://arxiv.org/abs/2409.20329
Autor:
Murat, Antoine, Burgelin, Clément, Xygkis, Athanasios, Zablotchi, Igor, Aguilera, Marcos K., Guerraoui, Rachid
Memory disaggregation is an emerging data center architecture that improves resource utilization and scalability. Replication is key to ensure the fault tolerance of applications, but replicating shared data in disaggregated memory is hard. We propos
Externí odkaz:
http://arxiv.org/abs/2409.16258
Blockchain promises to make online services more fault tolerant due to their inherent distributed nature. Their ability to execute arbitrary programs in different geo-distributed regions and on diverse operating systems make them an alternative of ch
Externí odkaz:
http://arxiv.org/abs/2409.13142
Autor:
Borges, Beatriz, Foroutan, Negar, Bayazit, Deniz, Sotnikova, Anna, Montariol, Syrielle, Nazaretzky, Tanya, Banaei, Mohammadreza, Sakhaeirad, Alireza, Servant, Philippe, Neshaei, Seyed Parsa, Frej, Jibril, Romanou, Angelika, Weiss, Gail, Mamooler, Sepideh, Chen, Zeming, Fan, Simin, Gao, Silin, Ismayilzada, Mete, Paul, Debjit, Schöpfer, Alexandre, Janchevski, Andrej, Tiede, Anja, Linden, Clarence, Troiani, Emanuele, Salvi, Francesco, Behrens, Freya, Orsi, Giacomo, Piccioli, Giovanni, Sevel, Hadrien, Coulon, Louis, Pineros-Rodriguez, Manuela, Bonnassies, Marin, Hellich, Pierre, van Gerwen, Puck, Gambhir, Sankalp, Pirelli, Solal, Blanchard, Thomas, Callens, Timothée, Aoun, Toni Abi, Alonso, Yannick Calvino, Cho, Yuri, Chiappa, Alberto, Sclocchi, Antonio, Bruno, Étienne, Hofhammer, Florian, Pescia, Gabriel, Rizk, Geovani, Dadi, Leello, Stoffl, Lucas, Ribeiro, Manoel Horta, Bovel, Matthieu, Pan, Yueyang, Radenovic, Aleksandra, Alahi, Alexandre, Mathis, Alexander, Bitbol, Anne-Florence, Faltings, Boi, Hébert, Cécile, Tuia, Devis, Maréchal, François, Candea, George, Carleo, Giuseppe, Chappelier, Jean-Cédric, Flammarion, Nicolas, Fürbringer, Jean-Marie, Pellet, Jean-Philippe, Aberer, Karl, Zdeborová, Lenka, Salathé, Marcel, Jaggi, Martin, Rajman, Martin, Payer, Mathias, Wyart, Matthieu, Gastpar, Michael, Ceriotti, Michele, Svensson, Ola, Lévêque, Olivier, Ienne, Paolo, Guerraoui, Rachid, West, Robert, Kashyap, Sanidhya, Piazza, Valerio, Simanis, Viesturs, Kuncak, Viktor, Cevher, Volkan, Schwaller, Philippe, Friedli, Sacha, Jermann, Patrick, Kaser, Tanja, Bosselut, Antoine
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning outcomes.
Externí odkaz:
http://arxiv.org/abs/2408.11841
Autor:
Guerraoui, Rachid, Kermarrec, Anne-Marie, Kucherenko, Anastasiia, Pinot, Rafael, de Vos, Martijn
The ability of a peer-to-peer (P2P) system to effectively host decentralized applications often relies on the availability of a peer-sampling service, which provides each participant with a random sample of other peers. Despite the practical effectiv
Externí odkaz:
http://arxiv.org/abs/2408.03829
Autor:
Robbani, Irfan, Reisert, Paul, Inoue, Naoya, Pothong, Surawat, Guerraoui, Camélia, Wang, Wenzhi, Naito, Shoichi, Choi, Jungmin, Inui, Kentaro
Prior research in computational argumentation has mainly focused on scoring the quality of arguments, with less attention on explicating logical errors. In this work, we introduce four sets of explainable templates for common informal logical fallaci
Externí odkaz:
http://arxiv.org/abs/2406.12402
Autor:
Aguilera, Marcos K., Burgelin, Clément, Guerraoui, Rachid, Murat, Antoine, Xygkis, Athanasios, Zablotchi, Igor
Data centers increasingly host mutually distrustful users on shared infrastructure. A powerful tool to safeguard such users are digital signatures. Digital signatures have revolutionized Internet-scale applications, but current signatures are too slo
Externí odkaz:
http://arxiv.org/abs/2406.07215
Batch normalization has proven to be a very beneficial mechanism to accelerate the training and improve the accuracy of deep neural networks in centralized environments. Yet, the scheme faces significant challenges in federated learning, especially u
Externí odkaz:
http://arxiv.org/abs/2405.14670
Autor:
Allouah, Youssef, Guerraoui, Rachid, Gupta, Nirupam, Jellouli, Ahmed, Rizk, Geovani, Stephan, John
Byzantine-resilient distributed machine learning seeks to achieve robust learning performance in the presence of misbehaving or adversarial workers. While state-of-the-art (SOTA) robust distributed gradient descent (Robust-DGD) methods were proven th
Externí odkaz:
http://arxiv.org/abs/2405.14432
Autor:
Allouah, Youssef, Koloskova, Anastasia, Firdoussi, Aymane El, Jaggi, Martin, Guerraoui, Rachid
Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Y
Externí odkaz:
http://arxiv.org/abs/2405.01031