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The internet of medical things (IoMT) ecosystem is highly vulnerable to malware attacks due to the vast number of connected devices and their continuous collection, transmission, and processing of sensitive data. Inadequate device management often makes each device a potential entry point, enabling malware to spread rapidly across networks with minimal detection. Given the resource constraints, privacy concerns, and distributed nature of IoT devices, there is a pressing need for lightweight and adaptive intrusion detection models. This paper proposes a federated learning (FL) based framework enhanced with TinyGAN, where the generator produces synthetic data to improve malware detection. The federated approach enables continuous, decentralized learning, allowing the model to adapt to emerging threats without requiring centralized retraining, thereby preserving privacy and reducing computational overhead. Experimental evaluations demonstrate significant improvements in both detection accuracy and efficiency compared to conventional centralized techniques. After 20 training rounds, the proposed model achieved a precision of 99.30%, a recall of 100%, and an F1-score of 99.52%. These results highlight the scalability, privacy-preserving nature, and effectiveness of the framework, offering a practical advancement in securing IoT environments against malware attacks. An experimental analysis of the IoT-23 dataset reveals that FL with TinyGAN consistently outperforms traditional models, such as MLP and FNN/LSTM, in terms of accuracy, convergence rate, and resource consumption, thereby establishing its effectiveness for practical IoT malware detection.