Who to Trust? Aggregating Client Knowledge in Logit-Based Federated Learning
Viktor Kovalchuk, Nikita Kotelevskii, Maxim Panov, Samuel Horváth, Martin Takáč
公開日: 2025/9/18
Abstract
Federated learning (FL) usually shares model weights or gradients, which is costly for large models. Logit-based FL reduces this cost by sharing only logits computed on a public proxy dataset. However, aggregating information from heterogeneous clients is still challenging. This paper studies this problem, introduces and compares three logit aggregation methods: simple averaging, uncertainty-weighted averaging, and a learned meta-aggregator. Evaluated on MNIST and CIFAR-10, these methods reduce communication overhead, improve robustness under non-IID data, and achieve accuracy competitive with centralized training.