Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation
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Computer Science > Machine Learning
Title:Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation
Abstract:We rethink Federated Learning (FL) from a nested learning perspective, framing the core challenge as how to collaboratively learn optimization rules, not just static models, to tackle Non-IID client data. To address this, we propose Federated Nested Learning (FedNL), a novel framework that reformulates FL as a three-level nested optimization system. FedNL embeds Titans-based linear attention into FL, enabling clients to perform lightweight, zero-shot test-time adaptation by treating a delta rule as an online gradient step. Experiments on Non-IID MMLU and long-context benchmarks show that FedNL achieves competitive performance in short-context reasoning, enhances the performance of long-context retrieval and streaming Cross-Entropy, and maintains constant inference memory.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.16350 [cs.LG] |
| (or arXiv:2605.16350v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16350
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