arXiv Machine Learning October 19, 2026 By Ali Sarabadani, Massimo Piccardi, Mehran Abolhasan, Farzad Tofigh, Ian Zhou, Justin Lipman

FederatedBytes: Tokenizer-Free, Byte-Level Small Language Models for Efficient Multimodal Federated Learning

Read the original on arXiv Machine Learning →

FederatedBytes is a small language model that processes text and multimodal data at the byte level, eliminating the need for tokenization. It uses a linear attention mechanism to keep memory and computational demands low, making it suitable for federated learning scenarios. Experiments on multilingual sentiment analysis tasks show that FederatedBytes outperforms models like ByT5 and LSTM, achieving higher accuracy while reducing memory and communication overheads.

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