Fed101
cd Fed101/algorithm/FedAVG &
python fedavg-main.py
-dataset
femnist
-model
femnist
--lr
0.03
--lr-decay
0.99
--decay-step
1
--batch-size
10
--clients-per-round
10
--num-rounds
1000
--seed
12
--epoch
5
--eval-interval
1
--note
run_1_seed_12
python fedavg-main.py
-dataset femnist
-model femnist
--lr 0.03
--lr-decay 0.99
--batch-size 10
--clients-per-round 10
--num-rounds 1000
--seed 24
--epoch 5
--eval-interval 1
--note run_2_seed_24
python fedavg-main.py -dataset femnist -model femnist --lr 0.03 --lr-decay 0.99 --batch-size 10 --clients-per-round 10 --num-rounds 1000 --seed 24 --epoch 5 --eval-interval 1 --note run_2_seed_24
Dataset Overview
dataset |
task |
metric |
client |
training set |
mean|std|skewness |
test set |
mean|std|skewness |
partition |
link |
MNIST |
10 clf |
acc |
1000 |
61664 |
61.664|144.63|24751822 |
7371 |
7.371|16.0772|34058.3 |
power law |
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FEMNIST |
62 clf |
acc |
3400 |
671585 |
197.53|76.681|391488.3 |
77483 |
22.7891|8.5105|533.892 |
realistic partition |
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CIFAR10 |
10 clf |
acc |
100 |
50000 |
500|147.22|-286980 |
10000 |
NA|NA|NA |
LDA |
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MNIST
Description
1000 clients, refer to fedprox
Model
CNN+FCNN
Algorithm & Result
FedAVG
FedProx
FedSP
FedMC
CIFAR10
Description
100 clients (10 groups), for certain group, the clients belong to it share the 90% of the specified class.
Model
CNN+FCNN
Algorithm & Result
FedAVG
#\T |
30 |
35 |
40 |
45 |
50 |
55 |
60 |
61 |
62 |
63 |
64 |
65 |
66 |
67 |
68 |
69 |
70 |
O|R |
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FedProx
#\T |
30 |
35 |
40 |
45 |
50 |
55 |
60 |
61 |
62 |
63 |
64 |
65 |
66 |
67 |
68 |
69 |
70 |
O|R |
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FedSP
#\T |
30 |
35 |
40 |
45 |
50 |
55 |
60 |
61 |
62 |
63 |
64 |
65 |
66 |
67 |
68 |
69 |
70 |
O|R |
1 |
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FedMC
gradient penalty = 0
critic = 20
with sigmoid
#\T |
30 |
35 |
40 |
45 |
50 |
55 |
60 |
61 |
62 |
63 |
64 |
65 |
66 |
67 |
68 |
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70 |
O|R |
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FEMNIST
Overview
Description
Partition dataset based on the writer of the digit/character.
Sample clients based on the number of samples it has(>=350).
original dataset: 3500 clients and 785697 samples.
sampled subset: 503 clients, 193081 samples
Model
CNN+FCNN
Hyper-parameters
- clients/round: 10/503
- epoch: 5
- batch-size: 10
- lr: 0.03
- lr-decay: 0.99
- decay-step: 1
- rounds: 1000
Algorithm & Result
FedAVG
#\T |
50 |
60 |
65 |
70 |
75 |
80 |
81 |
82 |
83 |
84 |
85 |
R|O |
Note |
1 |
9|51.42 |
13|60.43 |
17|65.46 |
23|70.41 |
38|75.09 |
89|80.21 |
104|81.08 |
138|82.26 |
204|83.07 |
316|84.08 |
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600|84.53 |
seed=12 |
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FedProx
#\T |
50 |
60 |
65 |
70 |
75 |
80 |
81 |
82 |
83 |
84 |
85 |
O|R |
Note |
1 |
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FedSP
#\T |
50 |
60 |
65 |
70 |
75 |
80 |
81 |
82 |
83 |
84 |
85 |
O|R |
Note |
1 |
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FedMC
gradient penalty = 0
critic = 20
with sigmoid
#\T |
50 |
60 |
65 |
70 |
75 |
80 |
81 |
82 |
83 |
84 |
85 |
O|R |
Note |
1 |
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