Collaborative deep learning (DL) allows distributed clients to jointly train models without sharing raw data, addressing privacy concerns in centralized methods. However, malicious participants can still compromise privacy. An attacker can use a Generative Adversarial Network (GAN) to reconstruct other clients’ data during training. Such cross-client attacks are particularly stealthy and difficult to defend against.This work proposes DeGain, an active detection defense designed to counter cross-client GAN-based data inversion attack. DeGain can actively identify attackers who maliciously manipulate their local models uploaded to the server before updating the global model. The key insight is that the tampered local model exhibits distinguishable characteristics that can be captured by a meta-classifier. DeGain leverages this by using a meta-classifier to differentiate between malicious and benign local model updates, extracting meta-data from the local model parameters to feed into the meta-classifier. Through extensive evaluations on two datasets of MNIST and AT&T demonstrate desirable detection accuracy up to 90%. Importantly, DeGain successfully defeats the GAN-based data inversion by the attacker, affirming its privacy-preserving capability. In addition, the utility of the global model is nearly unaffected.