sqs-fl-framework 1.3.1

Model-agnostic Federated Learning framework with Amazon SQS event bus

fl-framework

A model-agnostic Federated Learning framework with an Amazon SQS event bus.
Bring your own model — the framework handles the orchestration.

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Features

Feature	Description
Model-agnostic	Wrap any model (scikit-learn, PyTorch, XGBoost, ensembles) with a simple interface
SQS event bus	Clients and server communicate via Amazon SQS long-polling — no direct network connections between nodes
FedAvg aggregation	Sample-weighted averaging for float parameters; quality-weighted selection for non-averageable blobs (e.g. tree models)
Uniform aggregation	Optional equal-weight strategy regardless of local dataset size
In-process simulation	Run full FL rounds without SQS or a real backend — ideal for testing and research
Dynamic data loading	train_data_fn / eval_data_fn callables are invoked per round — supports live DB queries
Auto round advancement	Server can automatically start the next round after aggregation
Round timeout	Configurable deadline; raises RoundTimeoutError if not enough clients submit in time
Async client	FLClient.start_async() runs in a background thread so it doesn't block your application
Round lifecycle hooks	on_round_start / on_round_end optional callbacks on the model for custom side-effects
Environment-variable config	Every setting has an env-var override — no code changes needed between environments
Typed exceptions	WeightTransportError, SQSError, AggregationError, RoundTimeoutError for granular error handling

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Installation

pip install -e .

Dependencies: numpy, pandas, requests, boto3.

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Core Concepts

Edge Client 1 ──┐                          ┌── Edge Client 1
Edge Client 2 ──┤── SQS ──► FL Server ────┤── Edge Client 2
Edge Client 3 ──┘   (WEIGHTS_SUBMIT)       └── Edge Client 3
                     (ROUND_START / MODEL_BROADCAST)
                            │
                     Backend REST API
                     (weight storage)

1. Server broadcasts ROUND_START to all client queues.
2. Each client trains locally and submits weights via WEIGHTS_SUBMIT.
3. Once enough clients have submitted, the server runs FedAvg and broadcasts MODEL_BROADCAST.
4. Repeat.

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Quick Start

Step 1 — Implement the FLModel interface

import numpy as np
import pandas as pd
from fl_framework import FLModel

class MyLinearModel(FLModel):
    MODEL_ID = "my_linear_v1"          # unique ID, matches server config

    def __init__(self):
        from sklearn.linear_model import Ridge
        self._model = Ridge()

    def get_weights(self) -> dict[str, np.ndarray]:
        """Return model parameters as numpy arrays."""
        return {
            "coef":      self._model.coef_,
            "intercept": np.array([self._model.intercept_]),
        }

    def set_weights(self, weights: dict[str, np.ndarray]) -> None:
        """Apply aggregated global weights."""
        self._model.coef_      = weights["coef"]
        self._model.intercept_ = weights["intercept"][0]

    def train(self, data: pd.DataFrame, **kwargs) -> dict:
        """Local training pass. Return a metadata dict."""
        X, y = data.drop(columns=["target"]), data["target"]
        self._model.fit(X, y)
        return {"samples": len(data)}

    def evaluate(self, data: pd.DataFrame) -> dict[str, float]:
        """Return a metrics dict. Must include at least one numeric key."""
        from sklearn.metrics import r2_score
        X, y = data.drop(columns=["target"]), data["target"]
        return {"r2": float(r2_score(y, self._model.predict(X)))}

    def serialize(self) -> bytes:
        import pickle
        return pickle.dumps(self._model, protocol=4)

    @classmethod
    def deserialize(cls, data: bytes) -> "MyLinearModel":
        import pickle
        inst = cls.__new__(cls)
        inst._model = pickle.loads(data)
        return inst

---

Step 2 — Configure

from fl_framework import FLConfig

config = FLConfig(
    backend_url             = "https://api.example.com",
    sqs_queue_url           = "https://sqs.us-east-1.amazonaws.com/123/client-1-queue",
    sqs_server_queue_url    = "https://sqs.us-east-1.amazonaws.com/123/server-queue",
    sqs_region              = "us-east-1",
    client_id               = "edge-node-1",
    model_id                = "my_linear_v1",
    min_clients_for_aggregation = 3,
    max_rounds              = 10,
    round_timeout_s         = 300,
)

All fields can also be set via environment variables (see table at the bottom).

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Step 3 — Run a Client

from fl_framework import FLClient
import pandas as pd

def get_train_data():
    return pd.read_csv("my_local_data.csv")

client = FLClient(
    model         = MyLinearModel(),
    config        = config,
    train_data_fn = get_train_data,
)

client.start()           # blocking — listens for SQS events until stop()
# OR
client.start_async()     # background thread
# ...later...
client.stop()

---

Step 4 — Run the Server

from fl_framework import FLServer

server = FLServer(
    config = config,
    client_queue_urls = [
        "https://sqs.us-east-1.amazonaws.com/123/client-1-queue",
        "https://sqs.us-east-1.amazonaws.com/123/client-2-queue",
        "https://sqs.us-east-1.amazonaws.com/123/client-3-queue",
    ],
    on_round_complete = lambda round_, weights, metrics: print(f"Round {round_} done: {metrics}"),
)

initial_weights = MyLinearModel().get_weights()
server.start_round(round_=0, global_weights=initial_weights)
server.start()           # blocking; auto-advances rounds

---

In-Process Simulation (no SQS, no backend)

Useful for research and testing — runs everything in a single Python process.

from fl_framework import FLClient, FLServer, FLConfig
from fl_framework.aggregator import ClientUpdate, Aggregator
import pandas as pd

data   = pd.read_csv("data.csv")
model  = MyLinearModel()
config = FLConfig()          # defaults are fine for simulation

# --- one round manually ---
client = FLClient(model=model, config=config)
metrics = client.run_round(
    round_          = 0,
    global_weights  = model.get_weights(),
    local_epochs    = 50,
    train_data      = data,
    eval_data       = data,
)
print(metrics)   # {"r2": 0.97, "samples": 200, ...}

# --- aggregate multiple clients ---
aggregator = Aggregator()
updates = [
    ClientUpdate("c1", weights_c1, num_samples=100, metrics={"r2": 0.97}),
    ClientUpdate("c2", weights_c2, num_samples=120, metrics={"r2": 0.95}),
]
global_weights = aggregator.aggregate(updates, strategy="fedavg")

---

FLModel Interface — all methods

Method	Required	Description
get_weights() → dict[str, ndarray]	✅	Extract current model parameters
set_weights(dict)	✅	Apply received aggregated parameters
train(df, **kwargs) → dict	✅	Run a local training pass
evaluate(df) → dict[str, float]	✅	Evaluate and return metrics
serialize() → bytes	✅	Serialise the full model to bytes
deserialize(bytes) → FLModel	✅	Reconstruct from bytes (classmethod)
on_round_start(round_, weights)	❌	Hook called before local training
on_round_end(round_, metrics)	❌	Hook called after local training

---

Environment Variables

Variable	Default	Description
FL_BACKEND_URL	http://localhost:8000	Backend REST API base URL
FL_BACKEND_API_KEY	(none)	Optional Bearer token
FL_SQS_QUEUE_URL	(empty)	Client's inbound SQS queue
FL_SQS_SERVER_QUEUE_URL	(empty)	Server's inbound SQS queue
FL_SQS_REGION	us-east-1	AWS region
FL_CLIENT_ID	(random uuid)	Stable identifier for this node
FL_MODEL_ID	default	Model identifier string
FL_ROUND_TIMEOUT_S	300	Seconds to wait before RoundTimeoutError
FL_MAX_ROUNDS	100	Stop after this many rounds

---

Aggregation Strategies

Strategy	Behaviour	When to use
"fedavg" (default)	Weighted average by num_samples	Standard FL — more data = more influence
"uniform"	Equal weight per client	All clients have equal trust regardless of data size

Note on tree models / non-averageable parameters: Any weight key whose numpy array has dtype=uint8 is treated as a serialised blob. The framework performs selection (picks the highest-weight client's blob) rather than averaging, since trees are not additively composable.