Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning
📰 ArXiv cs.AI
Learn how to scale federated learning models using serverless federated aggregation via gradient partitioning, overcoming memory limitations
Action Steps
- Implement GradsSharding to partition gradients across multiple aggregators
- Configure serverless platforms like AWS Lambda to handle partitioned gradients
- Test the scalability of the model using gradient partitioning
- Compare the performance of GradsSharding with existing architectures like lambda-FL and LIFL
- Apply gradient partitioning to real-world federated learning problems
Who Needs to Know This
Machine learning engineers and researchers working on federated learning models can benefit from this technique to scale their models, especially when dealing with large gradients
Key Insight
💡 Gradient partitioning can overcome memory limitations in serverless federated learning aggregation
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🚀 Scale your federated learning models with GradsSharding! 🤖
Key Takeaways
Learn how to scale federated learning models using serverless federated aggregation via gradient partitioning, overcoming memory limitations
Full Article
Title: Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning
Abstract:
arXiv:2604.22072v1 Announce Type: cross Abstract: Federated learning (FL) aggregation on serverless platforms faces a hard scalability ceiling: existing architectures (lambda-FL, LIFL) partition clients across aggregators, but every aggregator must hold the complete model gradient in memory. When gradients exceed the per-function memory limit (e.g., 10 GB on AWS Lambda), aggregation becomes infeasible regardless of tree depth or branching factor. We propose GradsSharding, which instead partition
Abstract:
arXiv:2604.22072v1 Announce Type: cross Abstract: Federated learning (FL) aggregation on serverless platforms faces a hard scalability ceiling: existing architectures (lambda-FL, LIFL) partition clients across aggregators, but every aggregator must hold the complete model gradient in memory. When gradients exceed the per-function memory limit (e.g., 10 GB on AWS Lambda), aggregation becomes infeasible regardless of tree depth or branching factor. We propose GradsSharding, which instead partition
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