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.
@article{arxiv.2605.16350,
title = {Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation},
author = {Hong Chen and Pengcheng Wu and Yuanguo Lin and Peilin Zhao and Xiuze Zhou and Fan Lin and Han Yu},
journal= {arXiv preprint arXiv:2605.16350},
year = {2026}
}