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Official Implementation of IJCAI'25 paper "Inconsistency-Based Federated Active Learning"

by Chen-Chen Zong, Tong Jin, Sheng-Jun Huang

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python main.py --gpu 0 --seed 1 --al_method ifal --model cnn4conv --dataset cifar10 --partition dir_balance --dd_beta 0.1 --num_users 10 --frac 1.0 --num_classes 10 --rounds 100 --local_ep 5 --reset random --query_ratio 0.05

python main.py --gpu 0 --seed 1 --al_method ifal --model cnn4conv --dataset cifar100 --partition dir_balance --dd_beta 0.1 --num_users 10 --frac 1.0 --num_classes 100 --rounds 100 --local_ep 5 --reset random --query_ratio 0.05

python main.py --gpu 0 --seed 1 --al_method ifal --model cnn4conv --dataset tinyimagenet --partition dir_balance --dd_beta 0.1 --num_users 10 --frac 1.0 --num_classes 200 --rounds 100 --local_ep 5 --reset random --query_ratio 0.05

Acknowledgement

Thanks to Kim et al. for publishing their code for LoGo. Our implementation is heavily based on their work.

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Official Implementation of IJCAI'25 paper "Inconsistency-Based Federated Active Learning"

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