# Missing Modalities

In preoperative multimodal cataract data, SLO images are often missing, and prior methods mostly suppressed the effect of the missing modality during fusion or filled it with zeros rather than explicitly recovering a task-relevant representation for it. This work treats missing SLO as proxy representation recovery in latent feature space: OCT image features and clinical text features condition a diffusion model whose reverse denoising estimates a sample-specific SLO proxy representation, which then enters the fusion prediction network. On 2,141 preoperative samples with a 58.80% SLO missing rate, the method reached 0.9714 accuracy under an absolute error within 0.1 LogMAR and a mean absolute error of 0.0423, versus 0.9625 and 0.0457 for the UML backbone baseline.

In preoperative multimodal cataract data, SLO images are often missing, and prior methods mostly suppressed the effect of the missing modality during fusion or filled it with zeros rather than explicitly recovering a task-relevant representation for it. This work treats missing SLO as proxy representation recovery in latent feature space: OCT image features and clinical text features condition a diffusion model whose reverse denoising estimates a sample-specific SLO proxy representation, which then enters the fusion prediction network. On 2,141 preoperative samples with a 58.80% SLO missing rate, the method reached 0.9714 accuracy under an absolute error within 0.1 LogMAR and a mean absolute error of 0.0423, versus 0.9625 and 0.0457 for the UML backbone baseline.

In multimodal federated learning, when clients observe only some modalities, aggregating by which modality is available drops encoders that still shape predictions. RASO instead picks each component's contributors by prediction dependence and local-optimizer membership, keeps momentum on encoders, and synchronizes the shared prediction block to its current aggregate. Across KU-HAR, CREMA-D and other datasets under two fusion architectures, concatenation raises KU-HAR macro-F1 from 41.30 under the availability rule to 49.13, while under masked attention RASO leads on UCI-HAR, KU-HAR and CREMA-D and FedOpt leads on CrisisMMD.

In multimodal federated learning, when clients observe only some modalities, aggregating by which modality is available drops encoders that still shape predictions. RASO instead picks each component's contributors by prediction dependence and local-optimizer membership, keeps momentum on encoders, and synchronizes the shared prediction block to its current aggregate. Across KU-HAR, CREMA-D and other datasets under two fusion architectures, concatenation raises KU-HAR macro-F1 from 41.30 under the availability rule to 49.13, while under masked attention RASO leads on UCI-HAR, KU-HAR and CREMA-D and FedOpt leads on CrisisMMD.