Federated Learning Frameworks

Updated 22 May 2026

Federated learning frameworks are software platforms enabling decentralized, privacy-preserving collaborative model training in regulated environments.
They vary in architectures, communication protocols, and privacy techniques, such as secure aggregation and differential privacy, impacting scalability and efficiency.
Comparative studies reveal trade-offs in performance, resource requirements, and compliance, guiding framework selection for research and production deployments.

Federated learning (FL) frameworks are software platforms that implement, orchestrate, and support the end-to-end process of decentralized collaborative model training in environments where direct data sharing is restricted due to privacy, security, or regulatory constraints. These frameworks vary in their system architectures, privacy guarantees, extensibility, communication protocols, and suitability for different deployment scales and compliance demands. Evaluation of such frameworks centers on scalability, communication and computation overhead, privacy and compliance, extensibility, and empirical model performance. Recent comparative studies of leading FL frameworks—including NVIDIA FLARE, Flower, and Owkin Substra—demonstrate the domain-driven trade-offs embedded in software design and system engineering decisions (Gupta et al., 27 Oct 2025). The following sections detail fundamental design principles, system architectures, privacy and compliance mechanisms, developer and operational considerations, and performance profiles of contemporary federated learning frameworks.

1. System Architectures and Communication Patterns

FL frameworks commonly instantiate a "classical client–server" (star) topology, where a central server (or “federator”) orchestrates the training process by distributing models and aggregating client updates, as seen in NVIDIA FLARE, Flower, and Owkin Substra (Gupta et al., 27 Oct 2025). However, architectural designs differ in orchestration granularity, communication middleware, and the degree of hierarchy or decentralization:

NVIDIA FLARE utilizes a robust, production-grade orchestrator (the “federator”), communicating with isolated Python-based clients via gRPC, with protobuf-encoded plans to ensure deterministic execution. This design optimizes for reliability and scaling to thousands of clients, at the cost of high per-round communication (≈200 MB/client/round).
Flower adopts a lightweight Pythonic server-client implementation, typically communicating over HTTP/2. It requires minimal client configuration, achieves communication efficiency (≈50 MB/client/round), but provides less orchestration automation, making it suitable for academic prototyping or research clusters.
Owkin Substra institutes a ledger-driven, permissioned client–server network, where distributed “executor” nodes fetch task manifests (Docker plus hyperparameters) and communicate via encrypted object stores. This is optimized for environments with stringent auditability, privacy, and regulated compliance, albeit with moderate scalability (limited to “dozens” of clients).

Although the Federated Averaging (FedAvg) algorithm is canonical, frameworks support various extensions—such as asynchronous participation, hierarchical aggregation, and decentralized or peer-to-peer variants—to address system heterogeneity and domain constraints (Ghimire et al., 22 Nov 2025, Mukherjee et al., 2024).

2. Privacy, Compliance, and Security Mechanisms

FL frameworks diverge in their native integration of privacy and compliance features, which are critical for deployment in regulated domains such as healthcare:

Encryption: All three benchmarked frameworks in (Gupta et al., 27 Oct 2025) provide encrypted communication channels (TLS/HTTPS/gRPC), with Substra extending encryption to storage at rest.
Secure Aggregation: Substra uniquely offers built-in secure aggregation, ensuring only encrypted client updates are aggregated. FLARE and Flower rely on optional or third-party extensions.
Differential Privacy (DP): Substra is the only framework with a dedicated DP engine and tunable privacy budget (ε), supporting out-of-the-box compliance with standards such as GDPR and HIPAA. FLARE lacks a built-in DP module, requiring users to implement custom clipping and noise injection; Flower depends on community-maintained DP packages.
Audit Logging: Substra provides comprehensive audit logs on a private ledger, tracking all data accesses, executions, and model requests.

The presence of built-in privacy mechanisms—especially DP and secure aggregation—is a decisive factor for deployments in highly regulated sectors, as recognized in the comparative narrative (Gupta et al., 27 Oct 2025).

3. Developer Experience and Extensibility

Framework design decisions directly impact the ease with which researchers and engineers can prototype, extend, and operate FL systems:

NVIDIA FLARE requires heavy configuration with YAML/protobuf “plan” files and tight integration with established MLOps tools, offering a plugin system for highly customizable strategies but with a steep initial learning curve.
Flower is optimized for minimal setup, enabling experiments with a single pip install and Python callback-based extension. This is favored for rapid academic prototyping, though less so for complex, production-grade workflows.
Owkin Substra depends on Docker manifests and a ledger-driven network setup, oriented toward repeatable, fully-audited, and privacy-critical workflows. Setup complexity is rated as “medium,” reflecting the overhead of compliance infrastructure.

Across frameworks, extensibility is typically realized via plugin systems (custom aggregators or strategies), configuration-based module selection, and language-level APIs that allow integration with major deep learning frameworks (e.g., PyTorch, TensorFlow) (Reina et al., 2021, Chen et al., 2021).

4. Communication and Scalability Trade-offs

Communication overhead, memory footprint, and resource scaling are pivotal differentiators in FL frameworks, as these directly influence both the economic viability and technical feasibility of deployments at scale:

Framework	Max Clients Supported	Comm. Overhead (per round)	Computation Overhead	Empirical PathMNIST Accuracy
NVIDIA FLARE	Thousands	≈ 200 MB / client	High (GPU server recommended)	~70%, 7 rounds to 75%
Flower	Hundreds–Thousands	≈ 50 MB / client	Moderate (single CPU server)	~70%, 9 rounds to 75%
Owkin Substra	Dozens	≈ 150 MB / client	Medium (bottleneck registry)	~60%, <75% in 12 rounds

FLARE achieves the fastest convergence at higher computational cost. Flower provides the most communication-efficient baseline compatible with large research clusters. Substra’s stringent privacy engineering introduces communication and orchestration overhead, limiting scalability but maximizing compliance (Gupta et al., 27 Oct 2025).

5. Empirical Benchmarking and Performance Profiles

The empirical study in (Gupta et al., 27 Oct 2025) employed the PathMNIST dataset to benchmark frameworks on model performance, convergence, overhead, and resource usage:

Final model accuracies for FLARE and Flower are approximately 70%, while Substra peaked at ~60%, converging below the 75% threshold within 12 rounds.
Resource utilization varies: FLARE exhibits high GPU and RAM requirements per client, while Flower’s lightweight approach enables CPU-only training for small labs or research.
Rounds to target accuracy: FLARE converges in 7 rounds, Flower in 9, while Substra did not reach the target threshold, in part due to privacy overheads.

Additional experimental results corroborate the observation that privacy enhancements (secure aggregation, DP) systematically impact both communication and convergence metrics, necessitating trade-off evaluations in real applications.

6. Use-Case Suitability and Selection Criteria

Optimal FL framework choice should be guided by application context:

Scalability and Production-Readiness: For large clinical or multi-institutional deployments where fault tolerance and orchestration reliability are paramount, frameworks like NVIDIA FLARE present clear advantages.
Rapid Prototyping: For empirical research and development where time-to-experiment is crucial and strict compliance is not required, Flower’s lightweight design and extensibility stand out.
Regulated Environments: Environments with explicit compliance requirements for data privacy and auditable model development should prefer frameworks like Owkin Substra, which implement end-to-end privacy, DP, and encrypted audit trails.

Frameworks converge on similar architectural invariants—client–server topologies, FedAvg-based algorithms—but the differentiation emerges in privacy plumbing, orchestration depth, resource scaling, and compliance affordances.

7. Future Directions and Open Challenges

The current generation of FL frameworks exemplifies the trade-offs between scalability, privacy, and usability. Core open challenges remain:

Balancing communication efficiency with privacy requirements—fine-grained differentially private mechanisms and secure aggregation raise overhead, impacting convergence and maximum supported clients.
Improving developer experience—reducing configuration overhead while maintaining extensibility and compliance for regulated deployments.
Supporting additional aggregation topologies—hierarchical and peer-to-peer schemes for extreme scaling or full decentralization.
Integrating formal privacy accounting and reporting—especially as FL matures in healthcare, finance, and other sensitive sectors.

Comprehensive benchmarking as in (Gupta et al., 27 Oct 2025) will remain essential for objectively guiding adoption and for informing the continuous evolution of federated learning software infrastructure.