Relic: Federated Conditional Textual Inversion with Prototype Alignment
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".