Relic: Federated Conditional Textual Inversion with Prototype Alignment

• Main Authors: Chen, Sijia, Su, Ningxin, Li, Baochun
• Format: Conference Proceeding
• Language: English
• Subjects: Federated learning; Privacy; Prototypes; Quality of service; Servers; Text to image; Training
• ISSN: 2766-8568
• DOI: 10.1109/IWQoS61813.2024.10682846

Text-to-image models can generate personalized images with unprecedented freedom by using a pseudo-word learned from a few images, using a novel technique called textual inversion. It is conceivable that, in the spirit of federated learning, multiple users wish to learn a pseudo-word based on their local images collaboratively. However, how a more effective pseudo-word can be trained in the context of federated learning remains unclear.In this paper, our experiments show that such federated textual inversion is neither secure nor feasible. First, once one client exposes its pseudo-word embedding to the server for aggregation, an attacker can directly generate similar images to this client. Second, training one shared pseudo-word without personalization hinders individuals from generating images that exhibit local characteristics. Finally, after global aggregation, the averaged pseudo-word embedding may lose learned concepts. Motivated by these insights, we propose Relic, a new framework that encompasses federated conditional textual inversion with prototype alignment. With privacy guarantees, Relic allows clients to learn personalized pseudo-words conditional on local samples while enforcing a globally consistent clustering of clients' pseudo-words into discriminable prototypes instead of averaging. The experiments conducted on both i.i.d. and extreme non-i.i.d. data demonstrate that Relic is able to achieve state-of-the-art performance as compared to baseline approaches.