The paper introduces FastFederatedLearning (FFL), an open‑source federated learning framework written in C/C++ that prioritizes performance and customizability. FFL allows users to define arbitrary communication graphs between clients and servers, and it consistently outperforms Intel OpenFL with speedups of 2.5x to 3.69x across x86‑64, ARM‑v8, and RISC‑V platforms. Future plans include a Python wrapper and middleware for diverse communication backends, aiming to support dynamic, evolving federations as long‑term services.
arXiv:2609.15681v1 Announce Type: cross Abstract: This paper presents a systematic examination and experimental comparison of the prominent Federated Learning (FL) frameworks FedML, Flower, Substra,...
The paper proposes two hybrid algorithms, FL+FSDP and FL+HSDP, that combine sharded data parallelism with federated learning-style aggregations to reduce communication overhead in large-scale AI training. By partitioning GPUs into loosely‑coupled federation groups, the methods keep inter‑group traffic minimal while maintaining a bounded global batch size. Experiments on a Llama3.1 8B model trained on 512 A100 GPUs show up to 8.04× faster data processing and 4.48 lower evaluation perplexity compared to conventional sharded DP.
The paper proposes two hybrid algorithms, FL+FSDP and FL+HSDP, that combine sharded data parallelism with federated learning-style aggregations to reduce communication overhead in large-scale AI training. By forming loosely‑coupled federation groups, the methods keep inter‑group traffic minimal while maintaining a bounded global batch size. Experiments on a Llama3.1 8B model trained on 512 A100 GPUs show up to 8.04× faster data processing and 4.48 lower evaluation perplexity compared to traditional sharded DP approaches.
arXiv:2608. 13844v1 Announce Type: cross Abstract: Large language models (LLMs) have become core components of cloud-based intelligent services in academia and industry, yet their training and deployment are hindered by high computational costs, data centralization, and privacy concerns.
arXiv:2502. 08829v2 Announce Type: replace Abstract: Federated learning (FL) with non-IID data often degrades client performance below local training baselines.
Flower Hub is a platform that allows researchers to publish, discover, and run federated learning (FL) benchmarks in a reproducible way. It packages benchmarks as executable, versioned applications with standardized metadata, pinned dependencies, and explicit evaluation workflows, enabling the same benchmark to run in both simulation and real deployment environments. The platform includes a multi-domain benchmark suite covering cross-silo and cross-device settings in areas such as medical imaging, finance, legal instruction tuning, phishing detection, and audio tagging, and it supports system-aware reporting of runtime and communication metrics.
arXiv:2606. 16891v1 Announce Type: cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics.
arXiv:2608. 09208v1 Announce Type: cross Abstract: Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL).
Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL). However, DFL suffers from convergence inefficiency under data heterogeneity due to the use of a uniform learning rate (LR) that ignores layer-specific optimization needs.
arXiv:2606. 06687v1 Announce Type: new Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers.
arXiv:2409. 15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns.
arXiv:2606. 03143v1 Announce Type: new Abstract: Modern LLM agents increasingly rely on skill libraries to handle complex tasks, making skill evolution a primary driver of self-improvement.