Research on Key Technologies for Cross-Cloud Federated Training of Large Language Models

• Large language model training demands extensive computational resources and data, straining single-cloud platforms and creating bottlenecks, latency issues, and cost pressures[1].Source: §1 · p. 1
• Cross-cloud federated training addresses these constraints by coordinating computational resources from multiple clouds for distributed large-dataset processing and synchronous parameter updates.Source: §1 · p. 1
• Its implementation must allocate cloud resources, optimize inter-cloud communication, protect data privacy and security during training[2], and accommodate heterogeneous hardware and computing capacities.Source: §1 · pp. 1–2
• The study analyzes existing technical frameworks, proposes solutions for large-model training, and evaluates their feasibility and performance experimentally.Source: §1 · p. 2
• Its proposed improvements target data privacy protection, communication optimization, and model aggregation to support efficient large-model training.Source: §1 · p. 2
• Related contributions inform this work through multimodal modeling [10], dynamic optimization for resource utilization [13], and managing data complexity [16] in cross-cloud federated training for large language models.Source: §1 · p. 2

• Cross-cloud federated training uses multiple cloud platforms to handle large-language-model training and leverage their combined computational resources[4].Source: §2 · p. 2
• Distributing data and computational tasks across clouds reduces the resource burden on any single platform and improves scalability and computational efficiency[5].Source: §2 · p. 2
• Local data processing provides privacy advantages for privacy-sensitive applications such as healthcare and finance[6].Source: §2 · p. 2

3.1. Data Partitioning and Distribution Strategy

• Dynamic data granularity, load balancing, encryption, differential privacy, and real-time monitoring jointly support efficient, secure, and flexible cross-cloud training while reducing costs.Larger partitions reduce communication frequency but can increase single-platform load, whereas smaller partitions reduce local computation but increase inter-cloud overhead; distribution adapts to capacity, bandwidth, and load.Source: §3.1 · p. 3

3.2. Cross-Cloud Communication Optimization Techniques

• Communication optimization reduces transmission volume and latency through compression, local updates, efficient protocols, multiplexing, and asynchronous communication, though asynchronous updates may cause convergence fluctuations.Protocols such as gRPC or QUIC are suited to high-latency, low-bandwidth environments, while update-frequency adjustments balance model accuracy against communication efficiency.Source: §3.2 · p. 4

3.3. Model Aggregation Algorithms and Optimization

• FedAvg weights local models by sample size but can converge slowly or lose accuracy under uneven data and incur synchronization overhead; dynamic weighting improves convergence and global-model performance.Dynamic weighting assigns greater contributions to more accurate local models, mitigating bias from uneven data distribution.Source: §3.3 · p. 5
• Dynamic weighted aggregation significantly improves global-model convergence speed and accuracy under uneven data distribution.Source: §3.3 · p. 6
• Asynchronous aggregation reduces communication latency and overhead while maintaining high model accuracy and improving computational-resource utilization [34].Cloud platforms update according to local computational capabilities and training progress rather than simultaneously.Source: §3.3 · pp. 5–6
• Gradient-based aggregation demonstrates better generalization in heterogeneous-data scenarios [37-38].Gradient aggregation also helps improve the global model’s generalization ability when local data are heterogeneous.Source: §3.3 · pp. 5–6
• Dynamic weighting, gradient aggregation, and asynchronous updates enable more efficient training and more accurate model results in complex cross-cloud environments [39-40].Source: §3.3 · p. 6

• Each cloud trains a local sub-model, the global model is updated with a selected aggregation algorithm, and experiments record overhead, accuracy, and convergence time across rounds.Source: §4 · p. 6
• The experiment uses AWS, Google Cloud, and Azure to train a pretrained large language model on WikiText-103 distributed across cloud platforms.Source: §4 · p. 6
• Dynamic weighted aggregation converges faster than FedAvg after 50 training rounds, while gradient aggregation provides higher accuracy for heterogeneous data.Source: §4 · pp. 6–7
• Gradient aggregation has the least communication overhead because it transmits smaller data volumes, reducing communication overhead and training time.Source: §4 · p. 7, Table 2 · p. 7
• Gradient aggregation achieves the highest convergence accuracy and lowest loss, demonstrating strong adaptability to heterogeneous data.Source: §4 · p. 7, Table 3 · p. 7
• Dynamic weighted aggregation balances communication overhead and training efficiency, making it suitable for uneven data distributions across clouds.Source: §4 · p. 7

5. Conclusion

• Cross-cloud federated training addresses single-cloud resource limitations but must accommodate computational resources, communication optimization, data privacy, and heterogeneous environments.These challenges define the main technical scope of the study.Source: §5 · p. 7
• Dynamic weighted aggregation, gradient aggregation, and asynchronous updates improve training efficiency, reduce communication overhead, and enhance global-model convergence and performance.These techniques form the study’s proposed optimization framework for cross-cloud federated training.Source: §5 · p. 7
• The proposed framework establishes a technical foundation for efficient large-scale language-model training and has broad application potential.The conclusion links these prospects to continued optimization of the key technologies.Source: §5 · pp. 7–8
• Cross-cloud federated training can extend large language models to a wider range of practical scenarios.This conclusion follows from the claimed resource and training-efficiency benefits.Source: §5 · pp. 7–8