A data sharing method for remote medical system based on federated distillation learning and consortium blockchain

Figures & data

Table 1. Comparison of related work with FedRMD.

Methods	Efficient communication	Decentralised training	Defended poison attacks	Designed for COVID-19
Nguyen et al. (2022)	✓	✓	✗	✗
Cheng et al. (2021)	✓	✗	✓	✗
Tan et al. (2021)	✓	✗	✗	✓
Kumar et al. (2021)	✗	✓	✓	✓
Zhou et al. (2020)	✗	✓	✓	✗
Gong et al. (2021)	✓	✗	✗	✓
Lu et al. (2020)	✓	✓	✗	✗
FedRMD	✓	✓	✓	✓

Figure 1. Framework of FL in remote medical system.

Figure 2. Overview of proposed method.

Table

Algorithm 1: FedRMD
Input: global model G; communication rounds T; reputation threshold Γ; peer nodes set P; hospital n’s reputation value $Rvalue{}_n$ and datasets $\{D_n\}$
Output: final global model G
Initialise: global model G to $p\in P$
for each communication round $t=1,\dots ,T\mathit{d}\mathit{o}$
$H_t\leftarrow$ p select hospitals based on the Γ and $Rvalue{}_n$
M $\leftarrow$ G $H_t$ download local model from $p$
for $n\in H_t$ in parallel do:
Local model update: $M_t^n=M_{t-1}^n-\xi \tilde{\mathrm{\nabla }}{L}_i$($M_{t-1}^n$)
Compute the logits: $Z_t(\theta )=\text{ softmax }(\theta )$ for $\theta \in D_{logit}$
Send soft label $Z_t$ to $p$
Consensus certification and generate a new block
end for
$p$ calculates the average soft label for each hospital $Z_s^n=\frac{1}{|C_t|}\sum _{i\in S_t}^{i\ne n}{Z}_t^i$
Record positive and negative interactions with $n$
$p$ updates the global model: $G=G_{t-1}-\xi \tilde{\mathrm{\nabla }}{\ell }_g$($G_{t-1}$)
Consensus certification and generates a new block
end for

Table 2. Hardware configuration.

CPU	AMD Ryzen 7 5800H
GPU	NVIDIA GeForce RTX 3060 Ti
SSD	1TB
Operating system	Ubuntu 20.04.3
Memory	DDR4 3200 8G*2

Table 3. Software configuration.

Python	3.8.3
Tensorflow	2.3.0
Hyperledger Fabric	2.3.1
Hyperledger Caliper	0.5.0
Docker	20.10.12
Go	1.15.11
numpy	1.22.2
Scikit-learn(sklearn)	1.0.3

Table 4. Hyperparameter list.

	Hyperparameter	Value
Shared parameters	Learning rate	0.02
	Optimiser	sgd
	Local update steps	15
	Batch size(B)	32
	Communication rounds(T)	200
	Total hospitals	10
FedGen	Generator Optimiser	adam
	Generator learning rate	10^−4
	Generator inference size	128
	User distillation batch size	32
FedDF	Ensemble Optimiser	adam
	Generator learning rate	10^−4
	Ensemble batch size	128
FedRMD	Ensemble Optimiser	adam
	Generator learning rate	0.002
	Ensemble batch size	32

Table 5. Blockchain evaluation standards.

Success rate	>95%
Transaction per second (TPS)	>200
Average latency	<1 s
Average response time	<0.5 s

Figure 3. Accuracy of different number of hospitals.

Figure 4. Time cost of different number of hospitals.

Figure 5. Training loss during poisoning attacks.

Figure 6. Performance report of Hyperledger Fabric.

Figure 7. Average latency of blocks.

Figure 8. Transactions per second of blocks.

Figure 9. Accuracy trends on the COVID-Chestxray dataset.

Figure 10. AUC scores on the COVID-Chestxray dataset.

Table 6. Different methods on COVID-Chestxray.

	COVID-Chestxray (α = 1)
Methods	Accuracy (%)	Recall (%)	Precision (%)	AUC (%)
FedAvg	82.73 ± 0.13	76.23 ± 0.61	80.11 ± 0.93	86.93 ± 0.23
FedDF	83.94 ± 0.31	81.13 ± 0.87	85.34 ± 0.74	90.22 ± 0.38
FedGen	84.41 ± 0.21	83.82 ± 0.71	88.27 ± 0.82	91.87 ± 0.21
FedRMD	87.33 ± 0.35	85.26 ± 0.76	91.19 ± 0.65	93.78 ± 0.19

Figure 11. Accuracy trends on the COVID-19 radiography dataset.

Figure 12. AUC scores on the COVID-19 radiography dataset.

Table 7. Different methods on COVID-19 radiography.

	COVID-19 radiography (α = 0.2)
Methods	Accuracy (%)	Recall (%)	Precision (%)	AUC (%)
FedAvg	54.31 ± 0.32	51.45 ± 0.82	62.23 ± 0.63	67.20 ± 0.33
FedDF	57.84 ± 0.19	56.23 ± 0.79	68.53 ± 0.58	70.94 ± 0.38
FedGen	62.36 ± 0.35	63.86 ± 0.52	70.88 ± 0.72	77.88 ± 0.41
FedRMD	66.53 ± 0.27	68.19 ± 0.66	73.16 ± 0.53	82.74 ± 0.39

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