Teleportation Links: Mitigating Catastrophic Forgetting in Decentralized Federated Learning
Bibliographic record
Abstract
Decentralized approaches are inspired by the self-organizing principles observed in natural and social systems. These methods offer a scalable and resilient framework for collaborative learning. Decentralized federated learning (DFL) uses these principles to avoid the dependency on a central controller. However, a key challenge arises from data heterogeneity. This often leads to catastrophic forgetting, where the model significantly loses its ability to remember and use previously learned information. This loss of knowledge can reduce the reliability of DFL in critical applications, such as autonomous systems, financial services, energy management, and transportation networks. To tackle these challenges, in this paper, we investigate how data distribution affects DFL performance, with a focus on how bias propagates across nodes. We also explore how local model learning rates affect the trade-off between learning stability and convergence speed. At the same time, we evaluate the performance drops at individual nodes. Furthermore, to improve connectivity and speed up knowledge sharing, we propose adding a limited number of teleportation links, which aim to reduce the average distance between pairs of nodes. Extensive experimental results demonstrate the effectiveness of this strategy, showing reduced catastrophic forgetting, faster convergence, and improved resilience across various scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".