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cs.LG2025
FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting
Tharuka Kasthuri Arachchige, Veselka Boeva, Shahrooz Abghari
This work focuses on improving the performance and fairness of Federated Learning (FL) in non IID settings by enhancing model aggregation and boosting the training of underperformi…
cs.LG2018★ 1 cited
Hoeffding Trees with nmin adaptation
Eva García-Martín, Niklas Lavesson, Håkan Grahn +2
Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy…