Federated Learning (FL) as a secure distributed learning frame gains interest
in Internet of Things (IoT) due to its capability of protecting private data of
participants. However, traditional FL systems are vulnerable to attacks such as
Free-Rider (FR) attack, which causes not only unfairness but also privacy
leakage and inferior performance to FL systems. The existing defense mechanisms
against FR attacks only concern the scenarios where the adversaries declare
less than 50% of the total amount of clients. Moreover, they lose effectiveness
in resisting selfish FR (SFR) attacks. In this paper, we propose a Parameter
Audit-based Secure and fair federated learning Scheme (PASS) against FR
attacks. The PASS has the following key features: (a) works well in the
scenario where adversaries are more than 50% of the total amount of clients;
(b) is effective in countering anonymous FR attacks and SFR attacks; (c)
prevents from privacy leakage without accuracy loss. Extensive experimental
results verify the data protecting capability in mean square error against
privacy leakage and reveal the effectiveness of PASS in terms of a higher
defense success rate and lower false positive rate against anonymous SFR
attacks. Note in addition, PASS produces no effect on FL accuracy when there is
no FR adversary.Comment: 8 pages, 5 figures, 3 table