6 papers
Equilibrium and Infeasibility: A new solution concept for games
Anne Reulke, Mikaël Touati, Rachid El-Azouzi
Addressing infeasibility in non-cooperative games has become an important topic, as many problems across different applications face this issue. In this paper, we propose a new sol…
FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching
Haoran Zhang, Cainã Figueiredo Pereira, Marie Siew +3
Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds. While reusing stale model updates from inactive clie…
Strategic Analysis of Just-In-Time Liquidity Provision in Concentrated Liquidity Market Makers
Bruno Llacer Trotti, Weizhao Tang, Rachid El-Azouzi +2
Liquidity providers (LPs) are essential figures in the operation of automated market makers (AMMs); in exchange for transaction fees, LPs lend the liquidity that allows AMMs to ope…
FedPLT: Scalable, Resource-Efficient, and Heterogeneity-Aware Federated Learning via Partial Layer Training
Ahmad Dabaja, Rachid El-Azouzi
Federated Learning (FL) has gained significant attention in distributed machine learning by enabling collaborative model training across decentralized system while preserving data…
Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
Haoran Zhang, Zejun Gong, Zekai Li +3
Federated learning (FL) allows edge devices to collaboratively train models without sharing local data. As FL gains popularity, clients may need to train multiple unrelated FL mode…
FedSV: Byzantine-Robust Federated Learning via Shapley Value
Khaoula Otmani, Rachid Elazouzi, Vincent Labatut
In Federated Learning (FL), several clients jointly learn a machine learning model: each client maintains a local model for its local learning dataset, while a master server mainta…