Embodied agents must reason and act from experience accumulated before future questions or goals are known, yet existing memory systems often treat such experience as flat text, retrieved clips, or static graphs. We introduce MEMORA, an embodied memory framework that forms persistent participant-specific world memory from continuous egocentric video. MEMORA instantiates a formation–consolidation lifecycle: online memory editing maintains typed stores for spatial context, object identity and state, and temporal action evidence, while offline consolidation abstracts repeated experience into reusable routines, habits, and preferences. We evaluate MEMORA with MEMORA-Bench, a 45-hour egocentric-video suite for retrospective memory faithfulness and prospective memory-grounded planning. Across four open-weight answer models, MEMORA improves over no-memory, flat-text, and graph-structured memory baselines under matched perception and retrieval. The gains are largest on out-of-distribution planning, where MEMORA improves robot-grounded planning score by up to , showing that formed memory can compose new goals from remembered procedures, objects, and preferences. A physical-robot demonstration further suggests that memory formed from human egocentric video can guide participant-grounded action without in-domain robot data.

To address data locality and privacy restrictions, Federated Learning (FL) has recently been adopted to fine-tune large language models (LLMs), enabling improved performance on various downstream tasks without requiring aggregated data. However, the repeated exchange of model updates in FL can result in prohibitively high communication costs, hindering the distributed learning process. To address this challenge, we propose EcoLoRA, a novel communication-efficient federated fine-tuning framework for LLMs. Leveraging the modular structure, we propose a round-robin segment sharing scheme, where each client uploads only a complementary LoRA segment per round to reduce network bandwidth. It is further combined with adaptive sparsification methods tailored to LoRA’s training dynamics and lossless encoding techniques. We conduct extensive evaluations on both question-answering and value-alignment tasks across multiple datasets and models. The results show that EcoLoRA significantly reduces communication overhead without compromising performance. For instance, it reduces communication time by up to 79% and total training time by up to 65%.